CARGA DE LIBRERIAS¶

In [1113]:
library(gplots)
library(foreign)
library(car)
library(plotly)
library(ggpubr)
library(ggplot2)
library(reprex)
library(dplyr)
library(readxl)
library(readr)
library(rpart)
library(glmulti)
library(caret)
library(InformationValue)
library(rvest)
library(stringi)
library(stringr)
library(dplyr)
library(fmsb)
library(dlookr)
library(ggrepel)
library(rattle)
library(corrplot)
library(factoextra)
library(mclust)
library(FactoMineR)
library(tidyr)
library(GGally)
library(gridExtra)
library(grid)
library(FSelector)
library(mlbench)
library(RRF)
library(wsrf)
library(Boruta)
options(warn=-1)
In [1068]:
options(repr.plot.width = 18, repr.plot.height = 10)

IMPORTACION Y LECTURA DE LOS DATOS¶

In [1070]:
options(repr.matrix.max.rows=600, repr.matrix.max.cols=200)
In [1071]:
datos <- as.data.frame(read_excel("C:/Users/34625/Downloads/PROYECTOS DE TRABAJO/Yankel Carolina Sena/fwddatayankelsena/original_yankel.xlsx",sheet='Hoja3'))
datos
A data.frame: 59 × 34
STATUSCYCLES_BETWEEN_PET1_PET2GENDERAGETNM_STAGEWBC_PRERBC_PREHB_PREPLT_PRECRP_PREALBUMIN_PRELDH_PREeGFR_PREAST_PREALT_PREK_PREBGL_PRE_PET1BMIBW_PRESPLEEN_FDG_UPT_PRE_PET1BM_UPT_PRE_PET1LIVER_PRE_PET1ESTIMATED_SPLEE_VOL_PRE_PET1DIAGNOSTICTREATMENTECOGPSCOMORBIDITIESCTCNCIACTION_TAKEN_TIME_BETWEE_PETdiasSLR_preBMLR_preOVERALL_TIME
<chr><dbl><chr><dbl><chr><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><chr><chr><chr><chr><chr><chr><dbl><dbl><dbl><dbl><dbl>
ALIVE 3MALE 54III 3.823.03 9.70146 3.672.13573.0 60.1 16.80 6.704.87 9322.9066.502.672.862.45316.5GINECOLOGICAL ICI SYMPTOMATIC BUT AMBULATORY HYPERLIPIDEMIA SEVERE DRUG INTERRUPTED 2 250.85869571.0144928 2
ALIVE 2FEMALE79IV 10.873.9311.50515 4.112.65158.0 57.3 13.50 8.003.9210816.4733.502.402.702.70102.3HEAD AND NECK ICI ASYMTOMATIC HYPERTENSION NO SIDE EFFECTS DRUG INTERRUPTED 4 01.00995020.930348350
ALIVE 3MALE 60III 8.404.41 8.56355 0.094.32 77.8 82.1 19.10 13.704.56 8720.1558.102.571.312.27 81.5LUNG CANCER ICI SYMPTOMATIC,<50% IN BED DURING THE DAYDIABETES MELLITUS MODERATE DOSE REDUCED 4 140.99557521.0884956 2
ALIVE 3MALE 76II 4.663.5511.70178 0.103.70195.0 65.7 17.70 15.303.8313522.8356.702.252.462.26224.4RENAL CANCER ICI ASYMTOMATIC HYPERTENSION NO SIDE EFFECTS DOSE NOT CHANGED 6 00.88114750.717213112
ALIVE 5FEMALE70II 5.865.1915.10343 7.263.94166.0 79.9 18.10 10.203.8311118.2941.702.482.522.55260.0MELANOMA ICI SYMPTOMATIC BUT AMBULATORY DIABETES MELLITUS NO SIDE EFFECTS DOSE NOT CHANGED 6 00.82692311.019230812
ALIVE 4MALE 54IV 2.954.4514.50167 0.254.16135.0 76.3 14.40 14.004.55 9827.1880.402.011.402.83198.4LUNG CANCER ICI ASYMTOMATIC CANCER RRECURRENCE HIGHT SEVERE DOSE NOT CHANGED 63631.13215860.577092518
ALIVE 2MALE 60III 4.254.8215.70220 0.034.47201.0 75.4 25.30 30.004.5510122.7672.102.372.802.76257.3LUNG CANCER ICI ASYMTOMATIC CHRONIC INFLAMMATIONNO SIDE EFFECTS DOSE NOT CHANGED 9 01.16157211.1877729 7
ALIVE 5MALE 69III 9.172.25 7.30581 4.702.73483.0 89.2 25.60 40.603.8516217.3644.101.982.201.49104.1HEAD AND NECK ICI SYMPTOMATIC,<50% IN BED DURING THE DAYDIABETES MELLITUS MODERATE DOSE NOT CHANGED 9 950.74822700.836879416
ALIVE 6MALE 64IV 9.804.3613.90357 0.604.38139.0 54.4 21.20 27.905.1511223.6563.602.662.722.29122.9HEAD AND NECK ICI SYMPTOMATIC BUT AMBULATORY HYPERLIPIDEMIA SLIGTHLY SIDE EFFCTSDOSE NOT CHANGED 10 280.71024730.494699665
ALIVE12MALE 72III 5.613.6611.60282 0.103.73198.0 57.5 20.30 20.304.6912822.8468.602.031.872.01142.5HEAD AND NECK ICI ASYMTOMATIC CHRONIC INFLAMMATIONNO SIDE EFFECTS DOSE NOT CHANGED 16 01.00000000.9090909 1
ALIVE29MALE 71III 4.514.4113.50176 0.303.97175.0 51.9 20.30 14.504.2910021.3458.102.191.972.83166.4HEAD AND NECK ICI ASYMTOMATIC HYPERTENSION NO SIDE EFFECTS ADDED OTHER TREATMEN21 00.80079681.071713141
ALIVE 6MALE 67III 7.493.6110.60153 0.443.83184.0 81.4 13.60 14.604.0910020.2956.602.202.002.20122.3LUNG CANCER ICI ASYMTOMATIC CANCER RRECURRENCE NO SIDE EFFECTS DOSE NOT CHANGED 25 01.13733910.987124511
ALIVE48MALE 60III 8.384.1211.00239 0.193.38130.0 76.5 15.30 16.303.9210029.1167.702.012.692.51183.7RENAL CANCER ICI ASYMTOMATIC HYPERLIPIDEMIA SLIGTHLY SIDE EFFCTSUNKNOW 257350.78148151.144444411
ALIVE20MALE 61IV 6.114.1012.10495 1.282.91125.0 67.7 20.10 20.004.58 8520.1254.902.052.901.53203.1MELANOMA ICI SYMPTOMATIC,<50% IN BED DURING THE DAYDIABETES MELLITUS NO SIDE EFFECTS UNKNOW 38 00.78671330.765734315
DEATH 2MALE 83II 3.453.9811.60198 0.203.73214.0 46.9 13.70 9.004.81 8424.4662.002.442.062.67 56.9MELANOMA ICI ASYMTOMATIC CANCER RRECURRENCE NO SIDE EFFECTS DOSE NOT CHANGED 11 00.76455030.8756614 4
DEATH 3MALE 68I 5.144.2211.00268 0.423.75253.0 65.7 27.80 16.203.9210024.4175.802.152.652.60 92.5MELANOMA ICI SYMPTOMATIC,<50% IN BED DURING THE DAYCANCER RRECURRENCE NO SIDE EFFECTS DOSE NOT CHANGED 2 00.80625000.706250013
DEATH18MALE 77II 4.693.34 9.60183 0.063.43125.0 39.8 11.90 3.203.9810418.0751.802.151.752.44262.6RENAL CANCER ICI ASYMTOMATIC CHRONIC INFLAMMATIONSEVERE ADDED STEROIDS 10 31.00000000.909090912
DEATH 4MALE 60III 8.664.8015.00247 1.293.81139.0 57.6 13.00 12.703.9011623.1464.302.743.043.41160.0LUNG CANCER ICI SYMPTOMATIC BUT AMBULATORY CHRONIC INFLAMMATIONMODERATE UNKNOW 154410.94230771.115384629
DEATH 7MALE 64IV 3.463.8514.60140 0.234.10172.0 51.9 18.60 11.703.75 9622.1265.902.112.362.82174.7HEAD AND NECK ICI SYMPTOMATIC,<50% IN BED DURING THE DAYDIABETES MELLITUS MODERATE DRUG INTERRUPTED 4 520.78066910.966542842
DEATH15MALE 67II 13.323.7011.3044218.632.18670.0 96.6 14.90 23.104.51 9514.0814.082.531.881.89117.8HEAD AND NECK ICI SYMPTOMATIC BUT AMBULATORY DIABETES MELLITUS SLIGTHLY SIDE EFFCTSDRUG INTERRUPTED 133641.27179491.128205121
DEATH 5MALE 83IV 4.503.8613.10158 0.364.19111.0114.0 13.00 9.504.1710020.0747.302.152.512.08281.0HEAD AND NECK ICI SYMPTOMATIC,<50% IN BED DURING THE DAYHYPERTENSION NO SIDE EFFECTS DOSE NOT CHANGED 8 00.98818900.909448821
DEATH 4FEMALE56II 3.754.0312.00143 3.874.47751.0 45.7252.20281.504.7110421.7960.402.292.412.98143.0MELANOMA ICI SYMPTOMATIC BUT AMBULATORY HYPERLIPIDEMIA HIGHT SEVERE DOSE REDUCED 92331.15920400.9552239 8
ALIVE 7MALE 65II 7.104.5810.50269 0.353.22159.0 51.7 27.50 26.504.65 9018.1850.102.402.832.86114.2PANCREAS CANCER CHEMOASYMTOMATIC DIABETES MELLITUS NO SIDE EFFECTS UNKNOW 1 00.89320390.854368927
ALIVE 5FEMALE55IV 7.065.0310.20260 0.064.29214.0 66.0 15.30 29.803.97 7725.6062.302.262.302.93145.3GINECOLOGICAL CHEMOSYMPTOMATIC,<50% IN BED DURING THE DAYHYPERLIPIDEMIA SEVERE DRUG INTERRUPTED 2 201.00000001.100000010
ALIVE 2FEMALE75III 4.424.3012.50209 0.063.40174.0 70.2 17.40 17.104.04 9825.9664.402.452.552.52134.9LUNG CANCER CHEMOSYMPTOMATIC BUT AMBULATORY HYPERTENSION MODERATE DOSE REDUCED 2 150.97368420.697368441
ALIVE 6MALE 74III 7.434.1211.40126 0.673.97205.0 61.1 15.80 9.103.82 9520.8551.602.002.202.00125.0PANCREAS CANCER CHEMOASYMTOMATIC HYPERTENSION MODERATE DOSE REDUCED 3 350.83916080.9895105 3
ALIVE 3FEMALE76IV 4.093.6411.50106 0.013.70199.0 63.1 21.60 16.104.2611620.9043.402.221.592.28 79.0LUNG CANCER CHEMOASYMTOMATIC DIABETES MELLITUS NO SIDE EFFECTS DOSE NOT CHANGED 3 01.00947871.161137432
ALIVE 3MALE 77III 9.014.0812.50463 0.044.10205.0 56.5 43.70 96.504.5211122.9460.202.682.302.59160.8LUNG CANCER CHEMOASYMTOMATIC HYPERTENSION HIGHT SEVERE DOSE REDUCED 3 61.04641351.257384015
ALIVE 4MALE 68II 7.344.14 9.60187 3.012.92214.0 94.2 17.90 13.904.1310621.5759.502.282.412.04 90.7LUNG CANCER CHEMOASYMTOMATIC HYPERTENSION NO SIDE EFFECTS DOSE NOT CHANGED 4 01.07111110.693333314
ALIVE 2MALE 68II 6.535.1615.40247 0.204.10157.0 58.4 17.90 15.504.4811935.8194.202.762.643.09178.5LUNG CANCER CHEMOSYMPTOMATIC BUT AMBULATORY DIABETES MELLITUS NO SIDE EFFECTS DOSE NOT CHANGED 4 01.08366531.007968115
ALIVE 3FEMALE86IV 7.993.8711.80144 6.593.96143.0 41.0 47.80 36.103.6912326.7559.002.341.762.60114.9UROTHELIAL CARCINOMACHEMOSYMPTOMATIC,<50% IN BED DURING THE DAYHYPERTENSION SEVERE DOSE REDUCED 5 120.76512460.871886124
ALIVE 2MALE 74II 5.374.4014.20214 0.024.10133.0 61.1 19.60 17.203.93 7419.6249.602.402.502.80112.4PANCREAS CANCER CHEMOSYMPTOMATIC,<50% IN BED DURING THE DAYHYPERLIPIDEMIA SEVERE DOSE REDUCED 5 00.90000000.6769231 6
ALIVE 3FEMALE52I 2.313.6811.60159 0.174.14183.0 65.8 18.70 18.004.75 9522.1956.301.982.032.31146.2EWING SARCOMA CHEMOASYMTOMATIC DIABETES MELLITUS NO SIDE EFFECTS DOSE NOT CHANGED 5 01.21621621.045045032
ALIVE 4FEMALE74IV 1.723.3511.60121 0.023.60149.0 96.4 21.80 27.004.50 9017.7840.001.962.322.08125.3HEAD AND NECK CHEMOSYMPTOMATIC BUT AMBULATORY HYPERTENSION MODERATE DOSE NOT CHANGED 6 10.85714290.892857123
ALIVE13FEMALE70IV 5.044.0911.90325 2.013.23494.0 79.7 38.20 23.204.48 8424.3354.602.313.201.98164.0GINECOLOGICAL CHEMOSYMPTOMATIC,<50% IN BED DURING THE DAYHYPERLIPIDEMIA NO SIDE EFFECTS UNKNOW 7 01.05947960.721189615
ALIVE 2MALE 62III 7.055.4315.60235 0.064.15178.0 67.4 24.70 29.204.10 9316.2447.202.331.922.01 84.6LUNG CANCER CHEMOASYMTOMATIC CHRONIC INFLAMMATIONNO SIDE EFFECTS DOSE NOT CHANGED 8 01.03474900.888030963
ALIVE 3MALE 79IV 5.693.9912.40193 0.153.75222.0 53.7123.10 29.804.2720120.6459.102.893.313.78104.2RENAL CANCER CHEMOASYMTOMATIC CHRONIC INFLAMMATIONNO SIDE EFFECTS DOSE NOT CHANGED 9 01.03404261.0851064 9
ALIVE14FEMALE69II 4.564.4313.70294 0.174.53193.0 79.0 23.20 32.903.9016118.5745.202.432.552.35 96.7PANCREAS CANCER CHEMOSYMPTOMATIC >50 % IN THE BED ATEROESCLEROSIS NO SIDE EFFECTS DOSE REDUCED 9 00.97254900.9882353 4
ALIVE10FEMALE65II 13.302.86 8.70140 0.604.32161.0 74.0 23.10 33.104.18 8316.5141.002.702.322.22 59.0GINECOLOGICAL CHEMOSYMPTOMATIC,<50% IN BED DURING THE DAYHYPERLIPIDEMIA SEVERE DOSE REDUCED 10 321.03365381.206730821
ALIVE 4MALE 74III 2.413.3610.20311 0.143.76242.0 73.5 32.80 33.504.43 8622.0863.802.442.592.78164.3LUNG CANCER CHEMOASYMTOMATIC HYPERTENSION MODERATE DOSE NOT CHANGED 12 361.08979591.1673469 3
ALIVE12MALE 76III 6.514.3213.30215 0.053.91184.0 55.2 15.20 10.604.4018126.8077.902.652.302.33261.4LUNG CANCER CHEMOSYMPTOMATIC BUT AMBULATORY ATEROESCLEROSIS SEVERE ADDED OTHER TREATMEN14 510.77385160.6961131 6
ALIVE 4FEMALE72III 1.523.7511.10244 0.023.88174.0 72.2 21.40 13.304.0211615.4339.202.892.071.88 50.8EWING SARCOMA CHEMOASYMTOMATIC CANCER RRECURRENCE NO SIDE EFFECTS ADDED OTHER TREATMEN17 01.33986931.895424820
ALIVE14FEMALE76III 1.672.65 8.30194 3.913.98 96.0 63.1 6.10 3.704.6917929.5668.302.482.982.37172.7GINECOLOGICAL CHEMOSYMPTOMATIC,<50% IN BED DURING THE DAYHYPERTENSION NO SIDE EFFECTS UNKNOW 19 01.33862430.994709020
ALIVE 4FEMALE56III 7.274.3413.90189 0.134.27182.0 73.5 26.10 14.304.18 8018.0348.202.482.201.95 96.5LUNG CANCER CHEMOSYMPTOMATIC BUT AMBULATORY DIABETES MELLITUS NO SIDE EFFECTS UNKNOW 20 00.88888891.0000000 1
ALIVE 4MALE 70III 4.864.3014.50214 0.594.04176.0 59.9 26.50 29.503.7826223.7568.402.203.502.40 81.8LUNG CANCER CHEMOSYMPTOMATIC,<50% IN BED DURING THE DAYCHRONIC INFLAMMATIONNO SIDE EFFECTS DOSE NOT CHANGED 20 01.32885911.4765101 3
ALIVE 4FEMALE75III 6.334.1913.00351 0.024.62178.0 72.3 22.30 18.104.2510025.7850.902.102.602.69 96.9GINECOLOGICAL CHEMOASYMTOMATIC HYPERTENSION SEVERE DOSE REDUCED 333120.91385770.7715356 5
DEATH 9MALE 81II 6.664.2712.70315 0.354.19204.0 77.2 25.50 19.504.67 8116.1739.102.113.092.70123.7GASTRIC CANCER CHEMOASYMTOMATIC CANCER RRECURRENCE MODERATE DRUG INTERRUPTED 9 860.76845640.808724822
DEATH 9MALE 73III 7.952.72 8.80132 6.374.30157.0210.6 53.30 34.803.1115016.9041.502.202.002.20210.3PANCREAS CANCER CHEMOSYMPTOMATIC BUT AMBULATORY HYPERLIPIDEMIA SEVERE DRUG INTERRUPTED 41950.80351910.891495616
DEATH 5MALE 77I 7.434.7215.00222 0.393.80151.0 69.2 24.60 21.204.2810125.2167.802.812.852.87116.0LUNG CANCER CHEMOASYMTOMATIC CHRONIC INFLAMMATIONNO SIDE EFFECTS DRUG WIHDRAWN 5 01.16666671.6161616 9
DEATH 6MALE 74I 4.714.6914.70170 0.553.52210.0 76.6 21.90 14.204.7114023.5160.402.582.263.20138.6GASTRIC CANCER CHEMOASYMTOMATIC CANCER RRECURRENCE NO SIDE EFFECTS DOSE NOT CHANGED 12 00.87769780.9316547 1
DEATH 6MALE 70I 5.704.3113.20200 0.213.94216.0 74.8 22.60 16.804.08 8316.6845.702.411.562.25112.0LUNG CANCER CHEMOSYMPTOMATIC BUT AMBULATORY HYPERLIPIDEMIA SEVERE DOSE REDUCED 12 500.97909410.993031413
DEATH 6MALE 63II 4.082.99 8.20163 0.183.68142.0 62.5 94.20114.804.9319617.8950.602.851.942.69145.3PANCREAS CANCER CHEMOSYMPTOMATIC,<50% IN BED DURING THE DAYDIABETES MELLITUS NO SIDE EFFECTS DOSE REDUCED 7 00.97222221.011904864
DEATH 5FEMALE61II 6.555.2115.80203 0.024.27214.0 53.8 21.10 28.503.97 7520.4450.702.722.532.51150.2GINECOLOGICAL CHEMOASYMTOMATIC HYPERLIPIDEMIA HIGHT SEVERE DOSE REDUCED 111141.11764711.181372512
DEATH 2MALE 79III 7.834.5513.40262 0.264.35196.0 71.3 24.30 27.004.3710917.3045.402.252.192.86191.5LUNG CANCER CHEMOSYMPTOMATIC BUT AMBULATORY CANCER RRECURRENCE SEVERE DOSE REDUCED 3 01.53723401.101063817
DEATH 3FEMALE68III 3.453.9712.80193 0.043.70226.0 84.5 21.40 20.504.41 9325.7854.202.162.603.33 90.6SARCOMA CHEMOASYMTOMATIC ATEROESCLEROSIS MODERATE DRUG INTERRUPTED 9 70.64864860.780780816
DEATH 6FEMALE67III 7.084.4612.20459 0.283.69221.0 73.7 27.81 27.184.3213019.0744.002.512.312.54 97.2HEAD AND NECK CHEMOSYMPTOMATIC BUT AMBULATORY DIABETES MELLITUS NO SIDE EFFECTS DOSE REDUCED 1 00.77133110.784982947
DEATH10MALE 87III 1.593.9412.00 78 3.343.21204.0 69.2 23.40 16.304.37 9423.7659.702.382.902.73148.4GASTRIC CANCER CHEMOSYMPTOMATIC BUT AMBULATORY HYPERTENSION SEVERE DOSE REDUCED 10 330.91666671.458333330
DEATH 4MALE 78III 7.244.9513.80210 0.774.18142.0 89.2 15.70 19.603.97 9419.8655.402.152.452.81146.9LUNG CANCER CHEMOBEDBOUND CHRONIC INFLAMMATIONMODERATE DRUG INTERRUPTED 8 70.85714290.878787932
DEATH 8FEMALE53I 3.974.2012.40222 0.023.95150.0 85.1 13.60 14.904.09 9419.2651.802.132.452.11173.0PANCREAS CANCER CHEMOSYMPTOMATIC BUT AMBULATORY HYPERLIPIDEMIA SLIGTHLY SIDE EFFCTSDOSE REDUCED 3 70.87179491.062271123

Exploracion inicial¶

In [1072]:
# Tipos de varibales                    
aux = as.data.frame(t(t(sapply(datos, class))))
colnames(aux) <- "Tipo de Variable"
aux$Numero_Columna = seq(nrow(aux))
aux
A data.frame: 34 × 2
Tipo de VariableNumero_Columna
<chr><int>
STATUScharacter 1
CYCLES_BETWEEN_PET1_PET2numeric 2
GENDERcharacter 3
AGEnumeric 4
TNM_STAGEcharacter 5
WBC_PREnumeric 6
RBC_PREnumeric 7
HB_PREnumeric 8
PLT_PREnumeric 9
CRP_PREnumeric 10
ALBUMIN_PREnumeric 11
LDH_PREnumeric 12
eGFR_PREnumeric 13
AST_PREnumeric 14
ALT_PREnumeric 15
K_PREnumeric 16
BGL_PRE_PET1numeric 17
BMInumeric 18
BW_PREnumeric 19
SPLEEN_FDG_UPT_PRE_PET1numeric 20
BM_UPT_PRE_PET1numeric 21
LIVER_PRE_PET1numeric 22
ESTIMATED_SPLEE_VOL_PRE_PET1numeric 23
DIAGNOSTICcharacter24
TREATMENTcharacter25
ECOGPScharacter26
COMORBIDITIEScharacter27
CTCNCIcharacter28
ACTION_TAKEN_character29
TIME_BETWEE_PETnumeric 30
diasnumeric 31
SLR_prenumeric 32
BMLR_prenumeric 33
OVERALL_TIMEnumeric 34
In [1073]:
overview(datos)  )

plot(overview(datos))
A overview: 16 × 3
divisionmetricsvalue
<chr><chr><dbl>
size observations 59
size variables 34
size values 2006
size memory size 23752
duplicatedduplicate observation 0
missing complete observation 59
missing missing observation 0
missing missing variables 0
missing missing values 0
data type numerics 25
data type integers 0
data type factors/ordered 0
data type characters 9
data type Dates 0
data type POSIXcts 0
data type others 0

ORDENAR Y LIMPIAR DATOS¶

Transformar tipos de variables¶

In [1074]:
# pasar variables a factor
datos$STATUS <- as.factor(datos$STATUS)
datos$GENDER <- as.factor(datos$GENDER)
datos$COMORBIDITIES <- as.factor(datos$COMORBIDITIES)
datos$CTCNCI <- as.factor(datos$CTCNCI)
datos$ACTION_TAKEN_ <- as.factor(datos$ACTION_TAKEN_)
datos$TNM_STAGE <- as.factor(datos$TNM_STAGE)
datos$DIAGNOSTIC <- as.factor(datos$DIAGNOSTIC)
datos$TREATMENT <- as.factor(datos$TREATMENT)
datos$ECOGPS <- as.factor(datos$ECOGPS)
In [1075]:
# vemos los cambios
aux = as.data.frame(t(t(sapply(datos, class))))
colnames(aux) <- "Tipo de Variable"
aux$Numero_Columna = seq(nrow(aux))
aux
A data.frame: 34 × 2
Tipo de VariableNumero_Columna
<chr><int>
STATUSfactor 1
CYCLES_BETWEEN_PET1_PET2numeric 2
GENDERfactor 3
AGEnumeric 4
TNM_STAGEfactor 5
WBC_PREnumeric 6
RBC_PREnumeric 7
HB_PREnumeric 8
PLT_PREnumeric 9
CRP_PREnumeric10
ALBUMIN_PREnumeric11
LDH_PREnumeric12
eGFR_PREnumeric13
AST_PREnumeric14
ALT_PREnumeric15
K_PREnumeric16
BGL_PRE_PET1numeric17
BMInumeric18
BW_PREnumeric19
SPLEEN_FDG_UPT_PRE_PET1numeric20
BM_UPT_PRE_PET1numeric21
LIVER_PRE_PET1numeric22
ESTIMATED_SPLEE_VOL_PRE_PET1numeric23
DIAGNOSTICfactor 24
TREATMENTfactor 25
ECOGPSfactor 26
COMORBIDITIESfactor 27
CTCNCIfactor 28
ACTION_TAKEN_factor 29
TIME_BETWEE_PETnumeric30
diasnumeric31
SLR_prenumeric32
BMLR_prenumeric33
OVERALL_TIMEnumeric34

Identificar la variable respuesta¶

In [1076]:
# Identificar variable respuesta
respuesta <- c("STATUS")
respuesta
'STATUS'

Identificar las variables cuantitativas de entrada¶

In [1077]:
# Identificar variables numericas de entrada
tipos_var <- t(t(sapply(datos, class)))
var_num <- colnames(datos)[tipos_var=="integer"|tipos_var=="numeric"]
var_num <- var_num[var_num!=respuesta]
as.data.frame(var_num)
A data.frame: 25 × 1
var_num
<chr>
CYCLES_BETWEEN_PET1_PET2
AGE
WBC_PRE
RBC_PRE
HB_PRE
PLT_PRE
CRP_PRE
ALBUMIN_PRE
LDH_PRE
eGFR_PRE
AST_PRE
ALT_PRE
K_PRE
BGL_PRE_PET1
BMI
BW_PRE
SPLEEN_FDG_UPT_PRE_PET1
BM_UPT_PRE_PET1
LIVER_PRE_PET1
ESTIMATED_SPLEE_VOL_PRE_PET1
TIME_BETWEE_PET
dias
SLR_pre
BMLR_pre
OVERALL_TIME

Identificar las variables cualitativas de entrada¶

In [1078]:
# Identificar variables cualitativas
tipos_var <- t(t(sapply(datos, class)))
var_cual <- colnames(datos)[tipos_var=="character"|tipos_var=="factor"]
var_cual <- var_cual[var_cual!=respuesta]
as.data.frame(var_cual)
A data.frame: 8 × 1
var_cual
<chr>
GENDER
TNM_STAGE
DIAGNOSTIC
TREATMENT
ECOGPS
COMORBIDITIES
CTCNCI
ACTION_TAKEN_

EXPLORACION UNIVARIADA DE LOS DATOS¶

Describir la variable respuesta¶

In [1079]:
# Tabla de frecuencias:

as.data.frame(table(datos$STATUS))
A data.frame: 2 × 2
Var1Freq
<fct><int>
ALIVE38
DEATH21
In [1080]:
# Barplot
barplot(table(datos$STATUS))

Descripcion variables cuantitativas de entrada¶

Descriptivo numerico variables numericas¶

In [1081]:
# Descripcion de variables cuantitativas:
d_uni<-dlookr::describe(datos)
d_uni
A tibble: 25 × 26
described_variablesnnameansdse_meanIQRskewnesskurtosisp00p01p05p10p20p25p30p40p50p60p70p75p80p90p95p99p100
<chr><int><int><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>
CYCLES_BETWEEN_PET1_PET2 590 7.0677966 7.4506755 0.96999533 4.5000000 3.5461362116.07974075 2.0000000 2.0000000 2.0000000 2.0000000 3.0000000 3.0000000 3.400000 4.0000000 5.0000000 5.800000 6.000000 7.500000 9.400000 14.000000 18.200000 36.980000 48.000000
AGE 590 69.3898305 8.6223786 1.12253807 12.5000000-0.19664396-0.5894118652.000000052.5800000 54.0000000 56.0000000 61.0000000 63.5000000 65.000000 68.0000000 70.0000000 72.800000 74.600000 76.000000 76.400000 79.000000 83.000000 86.420000 87.000000
WBC_PRE 590 5.9266102 2.5757621 0.33533566 3.2150000 0.62790197 0.82592662 1.5200000 1.5606000 1.7150000 2.8420000 3.9100000 4.1700000 4.504000 4.8960000 5.7000000 6.546000 7.184000 7.385000 7.626000 8.730000 9.907000 13.308400 13.320000
RBC_PRE 590 4.0798305 0.6534959 0.08507792 0.6950000-0.49264321 0.51697560 2.2500000 2.4820000 2.8460000 3.2780000 3.6520000 3.7250000 3.864000 3.9980000 4.1200000 4.294000 4.384000 4.420000 4.496000 4.846000 5.163000 5.302400 5.430000
HB_PRE 590 12.1925424 2.0760802 0.27028262 2.7000000-0.26425199-0.44892147 7.3000000 7.8220000 8.5340000 9.4400000 10.5600000 11.0500000 11.440000 11.6200000 12.1000000 12.660000 13.360000 13.750000 14.020000 15.000000 15.420000 15.742000 15.800000
PLT_PRE 590241.1016949107.532143513.99949266107.0000000 1.31603975 1.4904397278.000000094.2400000125.5000000140.0000000158.6000000168.5000000180.000000194.8000000214.0000000232.400000261.200000275.500000312.600000374.000000466.200000542.720000581.000000
CRP_PRE 590 1.3561017 2.9053565 0.37824520 0.6450000 4.1288316221.53706019 0.0100000 0.0158000 0.0200000 0.0200000 0.0600000 0.0750000 0.112000 0.1820000 0.2500000 0.358000 0.596000 0.720000 1.578000 3.950000 6.392000 12.035400 18.630000
ALBUMIN_PRE 590 3.8081356 0.5376016 0.06998976 0.4850000-1.28325605 1.78142566 2.1300000 2.1590000 2.7220000 3.1520000 3.4840000 3.6850000 3.700000 3.7680000 3.9400000 3.978000 4.124000 4.170000 4.222000 4.326000 4.470000 4.567800 4.620000
LDH_PRE 590210.5559322126.612196816.48350401 61.5000000 2.94110478 8.7512562277.800000088.3560000123.6000000132.4000000142.6000000150.5000000157.400000174.0000000182.0000000195.800000204.600000212.000000214.000000244.200000501.900000704.020000751.000000
eGFR_PRE 590 71.1915254 23.4414552 3.05181753 18.9000000 3.7452040321.2858224239.800000040.4960000 46.7800000 51.9000000 56.9800000 58.0000000 60.500000 65.7000000 69.2000000 73.260000 75.940000 76.900000 79.780000 89.200000 96.420000154.572000210.600000
AST_PRE 590 28.5340678 34.7113610 4.51903428 9.1000000 5.2378477731.12051661 6.1000000 9.4640000 13.0000000 13.6000000 15.3000000 16.3000000 17.780000 19.2000000 21.2000000 22.220000 24.480000 25.400000 26.260000 39.300000 57.390000177.322000252.200000
ALT_PRE 590 26.9200000 38.1925396 4.97224514 14.1000000 5.5783508335.34995715 3.2000000 3.4900000 7.8700000 9.4200000 13.5400000 14.1000000 14.540000 16.2200000 18.0000000 20.460000 27.000000 28.200000 29.620000 33.760000 46.190000184.814000281.500000
K_PRE 590 4.2564407 0.3753149 0.04886183 0.5650000-0.13117635 0.30294823 3.1100000 3.4464000 3.7770000 3.8300000 3.9200000 3.9700000 3.996000 4.1060000 4.2600000 4.370000 4.492000 4.535000 4.568000 4.710000 4.816000 5.022400 5.150000
BGL_PRE_PET1 590111.5084746 35.3081865 4.59673434 23.0000000 2.14777101 5.4538436674.000000074.5800000 79.7000000 83.0000000 88.8000000 93.0000000 94.000000 96.4000000100.0000000104.000000111.600000116.000000125.000000161.200000182.500000226.620000262.000000
BMI 590 21.5445763 4.0359947 0.52544175 5.5200000 0.78269751 1.3993553714.080000014.8630000 16.2330000 16.6460000 17.9740000 18.2350000 19.146000 20.1780000 21.3400000 22.646000 23.362000 23.755000 24.430000 26.118000 27.373000 32.185000 35.810000
BW_PRE 590 55.6759322 12.8068343 1.66730781 16.4500000-0.03340815 1.7495265214.080000025.3436000 39.1900000 41.4000000 45.3200000 47.2500000 49.800000 51.8000000 56.3000000 59.080000 61.360000 63.700000 65.000000 68.440000 76.010000 86.196000 94.200000
SPLEEN_FDG_UPT_PRE_PET1 590 2.3613559 0.2563703 0.03337657 0.3700000 0.36335000-0.78951320 1.9600000 1.9716000 1.9980000 2.0260000 2.1420000 2.1500000 2.194000 2.2520000 2.3400000 2.408000 2.480000 2.520000 2.608000 2.724000 2.814000 2.890000 2.890000
BM_UPT_PRE_PET1 590 2.3925424 0.4596385 0.05983984 0.6050000-0.08071006 0.02245594 1.3100000 1.3622000 1.5870000 1.8480000 2.0000000 2.0650000 2.200000 2.3020000 2.4100000 2.518000 2.600000 2.670000 2.752000 2.916000 3.101000 3.389800 3.500000
LIVER_PRE_PET1 590 2.4950847 0.4375495 0.05696409 0.5800000 0.25199343 0.57543184 1.4900000 1.5132000 1.8890000 1.9960000 2.0980000 2.2100000 2.264000 2.3540000 2.5100000 2.600000 2.718000 2.790000 2.824000 2.940000 3.213000 3.565400 3.780000
ESTIMATED_SPLEE_VOL_PRE_PET1590145.3847458 58.4264814 7.60647999 69.6500000 0.92842859 0.6255027050.800000054.3380000 77.0000000 84.0400000 96.8200000103.2000000112.160000122.4200000138.6000000146.760000164.180000172.850000180.580000230.980000261.520000295.910000316.500000
TIME_BETWEE_PET 590 9.6271186 7.6404060 0.99469613 8.0000000 1.68704563 3.31772530 1.0000000 1.0000000 2.0000000 2.8000000 4.0000000 4.0000000 5.000000 6.0000000 8.0000000 9.000000 10.600000 12.000000 13.400000 20.000000 25.000000 35.100000 38.000000
dias 590 57.1525424133.928289317.43597813 34.0000000 3.2949716912.07515448 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.000000 0.0000000 0.0000000 7.000000 26.800000 34.000000 50.400000202.600000363.100000564.480000735.000000
SLR_pre 590 0.9711074 0.1795212 0.02337167 0.2322534 0.78999558 0.67146681 0.6486486 0.6843759 0.7629179 0.7707561 0.8024302 0.8330420 0.857764 0.8945631 0.9725490 1.000000 1.034466 1.065295 1.100936 1.176577 1.329836 1.422762 1.537234
BMLR_pre 590 0.9821935 0.2419472 0.03149884 0.2486236 1.17315435 3.09672332 0.4946996 0.5424875 0.6916923 0.7044737 0.7833021 0.8456242 0.876912 0.9091625 0.9871245 1.006375 1.067936 1.094248 1.120513 1.191564 1.460151 1.733452 1.895425
OVERALL_TIME 590 19.1016949 15.8415592 2.06239534 15.0000000 1.35374471 1.56391033 1.0000000 1.0000000 1.9000000 3.0000000 6.0000000 8.5000000 10.400000 12.2000000 15.0000000 17.800000 21.600000 23.500000 29.400000 41.200000 51.300000 64.420000 65.000000

Graficos descriptivos variables numericas¶

In [1082]:
for (i in 1:length(var_num)) {
  name_i <- as.name(var_num[i])
  r <- list()
  
  # Histograma de densidad
  r[[1]]<-ggplot(datos, aes(x=!!name_i)) +
    geom_density(fill="#69b3a2", color="black", alpha=0.6)+
    theme_light() +
    #theme(legend.position = "none") +
    xlab("") +
    ylab("Densidad de Frecuencia")
  
  # Boxplot
  r[[2]]<-ggplot(data=datos, aes(y=!!name_i)) +
    geom_boxplot(size = 0.4) +
    theme_light() +
    theme(legend.position = "none")+
    xlab("")
  
  # Qqplot
  r[[3]]<-ggqqplot(datos, x = var_num[i],color = "#FF6666",add.params = list(color = "black"))+
    xlab("") + ylab("Cuartiles reales") +
    theme_minimal() +
    #ggtitle("qqplot") +
    theme(plot.title = element_text(hjust = 0.5))
  
  grid.arrange(r[[1]], r[[2]], r[[3]],
               nrow=1, ncol = 3,
               top = textGrob(var_num[i],gp=gpar(fontsize=16,font=3)))
}

Analisis outliers(no los quitamos pero quiza aporten informacion )¶

In [1083]:
# Analisis de outliers:
diag_out <- diagnose_outlier(datos)
diag_out
A data.frame: 25 × 6
variablesoutliers_cntoutliers_ratiooutliers_meanwith_meanwithout_mean
<chr><int><dbl><dbl><dbl><dbl>
CYCLES_BETWEEN_PET1_PET2 5 8.474576 26.000000 7.0677966 5.3148148
AGE 0 0.000000 NaN 69.3898305 69.3898305
WBC_PRE 2 3.389831 13.310000 5.9266102 5.6675439
RBC_PRE 2 3.389831 2.450000 4.0798305 4.1370175
HB_PRE 0 0.000000 NaN 12.1925424 12.1925424
PLT_PRE 610.169492492.500000241.1016949212.6415094
CRP_PRE 1220.338983 5.622500 1.3561017 0.2668085
ALBUMIN_PRE 610.169492 2.586667 3.8081356 3.9464151
LDH_PRE 5 8.474576594.200000210.5559322175.0333333
eGFR_PRE 2 3.389831162.300000 71.1915254 67.9947368
AST_PRE 610.169492102.383333 28.5340678 20.1737736
ALT_PRE 3 5.084746164.266667 26.9200000 19.5621429
K_PRE 1 1.694915 3.110000 4.2564407 4.2762069
BGL_PRE_PET1 711.864407191.714286111.5084746100.7115385
BMI 1 1.694915 35.810000 21.5445763 21.2986207
BW_PRE 2 3.389831 54.140000 55.6759322 55.7298246
SPLEEN_FDG_UPT_PRE_PET1 0 0.000000 NaN 2.3613559 2.3613559
BM_UPT_PRE_PET1 0 0.000000 NaN 2.3925424 2.3925424
LIVER_PRE_PET1 1 1.694915 3.780000 2.4950847 2.4729310
ESTIMATED_SPLEE_VOL_PRE_PET1 2 3.389831298.750000145.3847458140.0035088
TIME_BETWEE_PET 4 6.779661 30.250000 9.6271186 8.1272727
dias 1016.949153293.800000 57.1525424 8.8571429
SLR_pre 1 1.694915 1.537234 0.9711074 0.9613466
BMLR_pre 3 5.084746 1.662699 0.9821935 0.9457379
OVERALL_TIME 5 8.474576 57.800000 19.1016949 15.5185185
In [1084]:
# Plot analisis de outliers:
plot_outlier(datos)
In [1085]:
# Plot de normalidad:
plot_normality(datos)

Diagrama de correlacion variables numericas¶

In [1086]:
var_num
  1. 'CYCLES_BETWEEN_PET1_PET2'
  2. 'AGE'
  3. 'WBC_PRE'
  4. 'RBC_PRE'
  5. 'HB_PRE'
  6. 'PLT_PRE'
  7. 'CRP_PRE'
  8. 'ALBUMIN_PRE'
  9. 'LDH_PRE'
  10. 'eGFR_PRE'
  11. 'AST_PRE'
  12. 'ALT_PRE'
  13. 'K_PRE'
  14. 'BGL_PRE_PET1'
  15. 'BMI'
  16. 'BW_PRE'
  17. 'SPLEEN_FDG_UPT_PRE_PET1'
  18. 'BM_UPT_PRE_PET1'
  19. 'LIVER_PRE_PET1'
  20. 'ESTIMATED_SPLEE_VOL_PRE_PET1'
  21. 'TIME_BETWEE_PET'
  22. 'dias'
  23. 'SLR_pre'
  24. 'BMLR_pre'
  25. 'OVERALL_TIME'
In [1087]:
vec_var_num = c('CYCLES_BETWEEN_PET1_PET2',
                'AGE',
                'WBC_PRE',
                'RBC_PRE',
                'HB_PRE',
                'PLT_PRE',
                'CRP_PRE',
                'ALBUMIN_PRE',
                'LDH_PRE',
                'eGFR_PRE',
                'AST_PRE',
                'ALT_PRE',
                'K_PRE',
                'BGL_PRE_PET1',
                'BMI',
                'BW_PRE',
                'SPLEEN_FDG_UPT_PRE_PET1',
                'BM_UPT_PRE_PET1',
                'LIVER_PRE_PET1',
                'ESTIMATED_SPLEE_VOL_PRE_PET1',
                'TIME_BETWEE_PET',
                'dias',
                'SLR_pre',
                'BMLR_pre',
                'OVERALL_TIME')

options(repr.plot.width=16, repr.plot.height=10)

plot_correlate(datos[,vec_var_num],method = "spearman")

Coeficiente de correlacion por parejas¶

In [1088]:
# coeficiente de correlación
library(Hmisc)
vec_var_num = c('CYCLES_BETWEEN_PET1_PET2',
                'AGE',
                'WBC_PRE',
                'RBC_PRE',
                'HB_PRE',
                'PLT_PRE',
                'CRP_PRE',
                'ALBUMIN_PRE',
                'LDH_PRE',
                'eGFR_PRE',
                'AST_PRE',
                'ALT_PRE',
                'K_PRE',
                'BGL_PRE_PET1',
                'BMI',
                'BW_PRE',
                'SPLEEN_FDG_UPT_PRE_PET1',
                'BM_UPT_PRE_PET1',
                'LIVER_PRE_PET1',
                'ESTIMATED_SPLEE_VOL_PRE_PET1',
                'TIME_BETWEE_PET',
                'dias',
                'SLR_pre',
                'BMLR_pre',
                'OVERALL_TIME')

# Correlaciones por parejas:

flattenCorrMatrix <- function(cormat, pmat) {
  ut <- upper.tri(cormat)
  data.frame(
    row = rownames(cormat)[row(cormat)[ut]],
    column = rownames(cormat)[col(cormat)[ut]],
    cor  =(cormat)[ut],
    p = pmat[ut]
  )
}


# Calculamos la matriz de correlacion y visualizamos la correlacion de mayor intensidad
tcor<-rcorr(as.matrix(datos[,vec_var_num]),type = "spearman")
corr_data <- flattenCorrMatrix(tcor$r, tcor$P) %>%
    arrange(desc(abs(cor))) #%>%
    #filter(row=='Sales')
corr_data
options(warn = -1)
A data.frame: 300 × 4
rowcolumncorp
<chr><chr><dbl><dbl>
BMI BW_PRE 0.84391256100.000000e+00
AST_PRE ALT_PRE 0.74857886129.337642e-12
RBC_PRE HB_PRE 0.71524623281.956835e-10
SLR_pre BMLR_pre 0.49758164736.083159e-05
WBC_PRE PLT_PRE 0.41284175801.156029e-03
CYCLES_BETWEEN_PET1_PET2 TIME_BETWEE_PET 0.40599728391.420657e-03
RBC_PRE LIVER_PRE_PET1 0.39536447221.940379e-03
HB_PRE ALBUMIN_PRE 0.38739436842.435104e-03
BMI LIVER_PRE_PET1 0.37727605113.223264e-03
BW_PRE LIVER_PRE_PET1 0.37673174873.271446e-03
eGFR_PRE BW_PRE -0.37169370223.748621e-03
LDH_PRE AST_PRE 0.36328291244.683351e-03
eGFR_PRE LIVER_PRE_PET1 -0.35735999085.459380e-03
RBC_PRE ALBUMIN_PRE 0.35476139645.834072e-03
RBC_PRE PLT_PRE 0.35028933856.531908e-03
HB_PRE LIVER_PRE_PET1 0.34991449206.593589e-03
SPLEEN_FDG_UPT_PRE_PET1 BMLR_pre 0.33963838878.495206e-03
WBC_PRE CRP_PRE 0.32807413341.119017e-02
BW_PRE OVERALL_TIME -0.30578470981.851064e-02
BW_PRE ESTIMATED_SPLEE_VOL_PRE_PET1 0.30437196811.908778e-02
AST_PRE ESTIMATED_SPLEE_VOL_PRE_PET1-0.29694738002.237887e-02
BMI BM_UPT_PRE_PET1 0.29358120572.402225e-02
BM_UPT_PRE_PET1 LIVER_PRE_PET1 0.29183232102.491552e-02
eGFR_PRE BMI -0.29109188802.530206e-02
LDH_PRE OVERALL_TIME -0.28222081933.033945e-02
CYCLES_BETWEEN_PET1_PET2 LIVER_PRE_PET1 -0.28187372163.055247e-02
CYCLES_BETWEEN_PET1_PET2 CRP_PRE 0.27020709893.847434e-02
WBC_PRE K_PRE -0.26737405834.063519e-02
LDH_PRE ALT_PRE 0.25496215145.131459e-02
PLT_PRE BM_UPT_PRE_PET1 0.25406314215.216948e-02
BW_PRE BM_UPT_PRE_PET1 0.25404110185.219058e-02
CYCLES_BETWEEN_PET1_PET2 ESTIMATED_SPLEE_VOL_PRE_PET1 0.25361101885.260378e-02
TIME_BETWEE_PET OVERALL_TIME -0.25101535065.515436e-02
SPLEEN_FDG_UPT_PRE_PET1 SLR_pre 0.24739798045.887532e-02
AGE BGL_PRE_PET1 0.24441810446.209066e-02
eGFR_PRE SPLEEN_FDG_UPT_PRE_PET1 -0.24033332036.672641e-02
CYCLES_BETWEEN_PET1_PET2 dias 0.23247621547.642104e-02
WBC_PRE RBC_PRE 0.23109519987.823513e-02
ALBUMIN_PRE ALT_PRE 0.23087043217.853358e-02
BMI OVERALL_TIME -0.22971200288.008611e-02
WBC_PRE BMI -0.22293496218.966081e-02
CRP_PRE ALBUMIN_PRE -0.21972591099.449689e-02
dias SLR_pre -0.21938325379.502508e-02
LDH_PRE ESTIMATED_SPLEE_VOL_PRE_PET1-0.21625256529.995807e-02
HB_PRE BM_UPT_PRE_PET1 0.21439953281.029702e-01
WBC_PRE BW_PRE -0.21228903341.064858e-01
SPLEEN_FDG_UPT_PRE_PET1 LIVER_PRE_PET1 0.21224216131.065649e-01
BGL_PRE_PET1 dias -0.21070652461.091822e-01
RBC_PRE SPLEEN_FDG_UPT_PRE_PET1 0.20893451091.122634e-01
BMI ESTIMATED_SPLEE_VOL_PRE_PET1 0.20728250391.151957e-01
CYCLES_BETWEEN_PET1_PET2 HB_PRE -0.20276241671.235186e-01
WBC_PRE dias 0.20106408131.267611e-01
WBC_PRE ALT_PRE 0.20086213201.271510e-01
K_PRE BW_PRE 0.19975153881.293110e-01
AGE RBC_PRE -0.19594488741.369254e-01
ALBUMIN_PRE LDH_PRE -0.19324843951.425195e-01
CRP_PRE BM_UPT_PRE_PET1 0.19301199041.430181e-01
K_PRE SPLEEN_FDG_UPT_PRE_PET1 0.19274801041.435762e-01
CYCLES_BETWEEN_PET1_PET2 SLR_pre -0.19145797041.463273e-01
HB_PRE CRP_PRE -0.18523016721.601625e-01
CRP_PRE BGL_PRE_PET1 0.18015249401.721365e-01
ALT_PRE ESTIMATED_SPLEE_VOL_PRE_PET1-0.17942311071.739087e-01
WBC_PRE ESTIMATED_SPLEE_VOL_PRE_PET1-0.17768166341.781935e-01
AGE dias -0.17662111461.808401e-01
BGL_PRE_PET1 BW_PRE 0.17559490321.834279e-01
ESTIMATED_SPLEE_VOL_PRE_PET1dias 0.17517593221.844921e-01
CYCLES_BETWEEN_PET1_PET2 RBC_PRE -0.17424571891.868707e-01
HB_PRE BW_PRE 0.17169709811.935006e-01
RBC_PRE BW_PRE 0.17128912531.945772e-01
ALBUMIN_PRE SLR_pre 0.17095916631.954512e-01
PLT_PRE ALT_PRE 0.16800619602.033973e-01
LIVER_PRE_PET1 TIME_BETWEE_PET -0.16747519992.048501e-01
BGL_PRE_PET1 SPLEEN_FDG_UPT_PRE_PET1 0.16727625452.053963e-01
AST_PRE BMI -0.16604562172.087978e-01
HB_PRE BMI 0.16156331472.215226e-01
RBC_PRE BGL_PRE_PET1 -0.15833813752.310068e-01
RBC_PRE BM_UPT_PRE_PET1 0.15815088962.315659e-01
eGFR_PRE BGL_PRE_PET1 -0.15489583832.414353e-01
CYCLES_BETWEEN_PET1_PET2 SPLEEN_FDG_UPT_PRE_PET1 -0.15482378042.416570e-01
BGL_PRE_PET1 BMI 0.15368309402.451850e-01
WBC_PRE SPLEEN_FDG_UPT_PRE_PET1 0.15247771892.489511e-01
AST_PRE BW_PRE -0.15247475572.489604e-01
AST_PRE SPLEEN_FDG_UPT_PRE_PET1 0.14877350712.607702e-01
PLT_PRE SPLEEN_FDG_UPT_PRE_PET1 0.14741182292.652084e-01
ALBUMIN_PRE LIVER_PRE_PET1 0.14465933112.743336e-01
AGE BW_PRE -0.14421567122.758237e-01
SPLEEN_FDG_UPT_PRE_PET1 ESTIMATED_SPLEE_VOL_PRE_PET1-0.14407040932.763128e-01
SPLEEN_FDG_UPT_PRE_PET1 BM_UPT_PRE_PET1 0.14390982322.768541e-01
RBC_PRE CRP_PRE -0.14270468232.809390e-01
AGE K_PRE -0.14171379602.843274e-01
K_PRE SLR_pre 0.14153963342.849257e-01
HB_PRE ESTIMATED_SPLEE_VOL_PRE_PET1 0.14134963062.855794e-01
TIME_BETWEE_PET BMLR_pre 0.13927538782.927798e-01
ALT_PRE BMI -0.13922496942.929563e-01
CRP_PRE OVERALL_TIME 0.13857184672.952487e-01
K_PRE BMI 0.13802578502.971743e-01
ALT_PRE BW_PRE -0.13735862292.995381e-01
CYCLES_BETWEEN_PET1_PET2 eGFR_PRE 0.13690272363.011603e-01
CYCLES_BETWEEN_PET1_PET2 ALBUMIN_PRE -0.13596002253.045328e-01
AGE ALBUMIN_PRE -0.13530010753.069081e-01
BM_UPT_PRE_PET1 ESTIMATED_SPLEE_VOL_PRE_PET1 0.13527417563.070017e-01
ALBUMIN_PRE AST_PRE 0.13418698843.109416e-01
ALBUMIN_PRE dias 0.13238288513.175510e-01
RBC_PRE BMI 0.13080834653.233920e-01
HB_PRE SPLEEN_FDG_UPT_PRE_PET1 0.12951807323.282290e-01
ALBUMIN_PRE BMLR_pre 0.12884225293.307806e-01
LIVER_PRE_PET1 ESTIMATED_SPLEE_VOL_PRE_PET1 0.12875931703.310946e-01
WBC_PRE LIVER_PRE_PET1 -0.12760671713.354778e-01
SPLEEN_FDG_UPT_PRE_PET1 dias -0.12726560503.367819e-01
K_PRE dias -0.12655512293.395084e-01
K_PRE BGL_PRE_PET1 -0.12551746573.435151e-01
HB_PRE TIME_BETWEE_PET 0.12532259493.442708e-01
CYCLES_BETWEEN_PET1_PET2 BMI -0.12050685663.632736e-01
CYCLES_BETWEEN_PET1_PET2 LDH_PRE -0.11732577423.761695e-01
TIME_BETWEE_PET dias 0.11731979323.761940e-01
SLR_pre OVERALL_TIME -0.11706905893.772221e-01
RBC_PRE TIME_BETWEE_PET -0.11544859483.839068e-01
BMLR_pre OVERALL_TIME -0.11541163833.840601e-01
BMI BMLR_pre -0.11464391133.872524e-01
LDH_PRE K_PRE 0.11429156193.887227e-01
K_PRE LIVER_PRE_PET1 0.11421638923.890369e-01
ALBUMIN_PRE OVERALL_TIME -0.11401367013.898847e-01
AST_PRE BGL_PRE_PET1 0.11395814693.901171e-01
AGE LIVER_PRE_PET1 0.11324073583.931274e-01
PLT_PRE ESTIMATED_SPLEE_VOL_PRE_PET1-0.11318935053.933436e-01
ALBUMIN_PRE BM_UPT_PRE_PET1 -0.11063842294.041614e-01
ALT_PRE dias 0.10890607584.116056e-01
ALT_PRE BMLR_pre 0.10862703844.128121e-01
CRP_PRE ESTIMATED_SPLEE_VOL_PRE_PET1 0.10772360604.167321e-01
AGE ALT_PRE -0.10587226744.248315e-01
CYCLES_BETWEEN_PET1_PET2 BW_PRE -0.10562891594.259027e-01
AST_PRE LIVER_PRE_PET1 0.10456704434.305951e-01
CRP_PRE dias 0.10371071314.344004e-01
LIVER_PRE_PET1 BMLR_pre 0.10309805714.371344e-01
AGE eGFR_PRE -0.10148940874.443588e-01
WBC_PRE SLR_pre -0.10071017334.478820e-01
HB_PRE eGFR_PRE -0.09895780224.558613e-01
ALT_PRE SLR_pre 0.09863662274.573322e-01
CRP_PRE SLR_pre -0.09855964894.576851e-01
AST_PRE TIME_BETWEE_PET -0.09830112754.588714e-01
K_PRE TIME_BETWEE_PET 0.09611941604.689493e-01
AST_PRE dias -0.09606921684.691826e-01
CYCLES_BETWEEN_PET1_PET2 AST_PRE -0.09431724544.773631e-01
AST_PRE K_PRE 0.09329552724.821686e-01
PLT_PRE BMI -0.09189876394.887794e-01
BMI dias 0.09146839604.908258e-01
PLT_PRE eGFR_PRE 0.09117612684.922181e-01
RBC_PRE dias -0.09081255274.939530e-01
HB_PRE PLT_PRE 0.09017028654.970254e-01
BGL_PRE_PET1 ESTIMATED_SPLEE_VOL_PRE_PET1 0.08853339535.049006e-01
AGE BMLR_pre -0.08825600715.062414e-01
ALT_PRE OVERALL_TIME 0.08780782245.084117e-01
CRP_PRE BMLR_pre -0.08653567275.145977e-01
CRP_PRE eGFR_PRE 0.08648476545.148460e-01
CYCLES_BETWEEN_PET1_PET2 AGE -0.08604225535.170072e-01
eGFR_PRE K_PRE -0.08378157255.281190e-01
PLT_PRE BMLR_pre -0.08352573305.293840e-01
HB_PRE K_PRE -0.08273028975.333264e-01
eGFR_PRE dias 0.08263296425.338098e-01
HB_PRE LDH_PRE -0.08261575125.338953e-01
WBC_PRE ALBUMIN_PRE 0.08135524205.401754e-01
LDH_PRE BGL_PRE_PET1 -0.07980926655.479266e-01
AST_PRE BM_UPT_PRE_PET1 0.07965623655.486968e-01
AST_PRE BMLR_pre 0.07939152185.500302e-01
ALT_PRE LIVER_PRE_PET1 0.07901736165.519177e-01
LDH_PRE TIME_BETWEE_PET -0.07829251305.555829e-01
BW_PRE SLR_pre 0.07750646625.595707e-01
ALBUMIN_PRE BGL_PRE_PET1 -0.07606788655.669036e-01
HB_PRE dias -0.07566979555.689407e-01
AGE SLR_pre -0.07534201255.706205e-01
WBC_PRE LDH_PRE -0.07503763815.721825e-01
RBC_PRE OVERALL_TIME -0.07360942185.795379e-01
WBC_PRE eGFR_PRE 0.07335856585.808343e-01
PLT_PRE BW_PRE -0.07313829795.819737e-01
K_PRE ESTIMATED_SPLEE_VOL_PRE_PET1 0.06901289256.034986e-01
CYCLES_BETWEEN_PET1_PET2 WBC_PRE 0.06876313956.048128e-01
LDH_PRE dias 0.06847036216.063550e-01
AGE PLT_PRE -0.06565823186.212543e-01
PLT_PRE K_PRE 0.06545061346.223604e-01
LDH_PRE SPLEEN_FDG_UPT_PRE_PET1 0.06522597406.235582e-01
PLT_PRE AST_PRE 0.06508936276.242870e-01
CRP_PRE K_PRE -0.06488036846.254028e-01
BGL_PRE_PET1 SLR_pre 0.06449444816.274653e-01
ALBUMIN_PRE K_PRE -0.06422420746.289112e-01
BW_PRE TIME_BETWEE_PET 0.06418367716.291282e-01
eGFR_PRE BM_UPT_PRE_PET1 -0.06343359986.331494e-01
WBC_PRE BM_UPT_PRE_PET1 -0.06326820046.340375e-01
ALT_PRE BGL_PRE_PET1 0.06297257046.356262e-01
RBC_PRE K_PRE -0.06249634746.381888e-01
SPLEEN_FDG_UPT_PRE_PET1 OVERALL_TIME -0.06244606296.384597e-01
BW_PRE SPLEEN_FDG_UPT_PRE_PET1 0.06102998556.461059e-01
RBC_PRE LDH_PRE -0.06091964276.467033e-01
AGE BM_UPT_PRE_PET1 0.06072225706.477724e-01
PLT_PRE LDH_PRE 0.05957670856.539911e-01
TIME_BETWEE_PET SLR_pre 0.05891157506.576126e-01
BGL_PRE_PET1 LIVER_PRE_PET1 0.05860501996.592843e-01
BM_UPT_PRE_PET1 TIME_BETWEE_PET 0.05811531406.619583e-01
AGE CRP_PRE 0.05740177406.658621e-01
ALT_PRE SPLEEN_FDG_UPT_PRE_PET1 0.05731702356.663263e-01
WBC_PRE TIME_BETWEE_PET -0.05721081236.669083e-01
LDH_PRE SLR_pre 0.05706184276.677249e-01
ALT_PRE K_PRE 0.05656240986.704655e-01
eGFR_PRE ALT_PRE 0.05627648706.720364e-01
LIVER_PRE_PET1 dias 0.05510070666.785108e-01
BM_UPT_PRE_PET1 dias -0.05466645116.809079e-01
ALT_PRE TIME_BETWEE_PET -0.05410645896.840038e-01
LDH_PRE BMLR_pre 0.05391698526.850524e-01
BW_PRE dias 0.05026675487.053680e-01
BMI SLR_pre -0.04994520357.071677e-01
PLT_PRE OVERALL_TIME -0.04777294597.193670e-01
AGE AST_PRE 0.04690360237.242690e-01
PLT_PRE dias -0.04667763657.255449e-01
CRP_PRE LDH_PRE 0.04604359147.291292e-01
BGL_PRE_PET1 TIME_BETWEE_PET 0.04586771497.301244e-01
BM_UPT_PRE_PET1 OVERALL_TIME -0.04560080127.316357e-01
CRP_PRE ALT_PRE -0.04436581687.386412e-01
CRP_PRE BMI -0.04351641807.434718e-01
BGL_PRE_PET1 OVERALL_TIME 0.04339045597.441890e-01
PLT_PRE ALBUMIN_PRE -0.04333971757.444780e-01
PLT_PRE TIME_BETWEE_PET 0.04326885527.448816e-01
eGFR_PRE BMLR_pre -0.04296235707.466281e-01
AGE ESTIMATED_SPLEE_VOL_PRE_PET1-0.04254077057.490325e-01
WBC_PRE AST_PRE 0.04239244807.498790e-01
WBC_PRE BGL_PRE_PET1 0.04144488087.552938e-01
ALBUMIN_PRE TIME_BETWEE_PET 0.04064239467.598886e-01
WBC_PRE HB_PRE -0.03963869297.656473e-01
eGFR_PRE ESTIMATED_SPLEE_VOL_PRE_PET1-0.03887070397.700620e-01
PLT_PRE LIVER_PRE_PET1 -0.03884488367.702106e-01
eGFR_PRE AST_PRE -0.03809899607.745055e-01
ALT_PRE BM_UPT_PRE_PET1 0.03767794047.769330e-01
CRP_PRE LIVER_PRE_PET1 -0.03742323027.784025e-01
CYCLES_BETWEEN_PET1_PET2 OVERALL_TIME 0.03736138557.787594e-01
K_PRE BMLR_pre -0.03243109038.073531e-01
BMI TIME_BETWEE_PET 0.03239848128.075431e-01
BM_UPT_PRE_PET1 SLR_pre -0.03063387018.178411e-01
RBC_PRE eGFR_PRE -0.03060061388.180354e-01
AST_PRE OVERALL_TIME -0.02886598518.281889e-01
CYCLES_BETWEEN_PET1_PET2 ALT_PRE -0.02846632248.305323e-01
CYCLES_BETWEEN_PET1_PET2 BMLR_pre 0.02768460498.351201e-01
HB_PRE AST_PRE 0.02704124948.389000e-01
CYCLES_BETWEEN_PET1_PET2 BGL_PRE_PET1 0.02674920298.406170e-01
AGE OVERALL_TIME 0.02604304198.447718e-01
ESTIMATED_SPLEE_VOL_PRE_PET1TIME_BETWEE_PET -0.02519230918.497828e-01
LDH_PRE BW_PRE -0.02500803958.508690e-01
BGL_PRE_PET1 BMLR_pre 0.02332520088.608010e-01
K_PRE BM_UPT_PRE_PET1 0.02251198698.656083e-01
CRP_PRE TIME_BETWEE_PET 0.02203007798.684594e-01
LDH_PRE eGFR_PRE 0.02191072048.691658e-01
LDH_PRE BM_UPT_PRE_PET1 0.02178171518.699294e-01
dias OVERALL_TIME 0.02176135398.700499e-01
ESTIMATED_SPLEE_VOL_PRE_PET1SLR_pre -0.02101263988.744842e-01
LDH_PRE LIVER_PRE_PET1 0.02080044468.757417e-01
AGE WBC_PRE -0.02021740168.791983e-01
WBC_PRE OVERALL_TIME 0.02017868858.794279e-01
AGE TIME_BETWEE_PET 0.02016278358.795222e-01
CYCLES_BETWEEN_PET1_PET2 K_PRE 0.02016040288.795363e-01
LIVER_PRE_PET1 OVERALL_TIME 0.01921558638.851428e-01
PLT_PRE BGL_PRE_PET1 0.01907074598.860028e-01
HB_PRE BGL_PRE_PET1 -0.01644188559.016331e-01
AGE SPLEEN_FDG_UPT_PRE_PET1 0.01637617399.020243e-01
CRP_PRE BW_PRE -0.01590969309.048020e-01
CYCLES_BETWEEN_PET1_PET2 PLT_PRE 0.01566282879.062725e-01
RBC_PRE SLR_pre 0.01557728009.067821e-01
ESTIMATED_SPLEE_VOL_PRE_PET1BMLR_pre 0.01496244789.104460e-01
AGE BMI 0.01495071109.105160e-01
ALBUMIN_PRE BMI -0.01493780869.105929e-01
RBC_PRE ESTIMATED_SPLEE_VOL_PRE_PET1-0.01467064129.121856e-01
K_PRE OVERALL_TIME -0.01462544409.124551e-01
BM_UPT_PRE_PET1 BMLR_pre 0.01423477179.147848e-01
ALBUMIN_PRE BW_PRE 0.01401739449.160814e-01
AGE LDH_PRE -0.01388645589.168625e-01
RBC_PRE ALT_PRE 0.01287371779.229066e-01
CYCLES_BETWEEN_PET1_PET2 BM_UPT_PRE_PET1 0.01261462019.244536e-01
eGFR_PRE TIME_BETWEE_PET 0.01258261109.246447e-01
dias BMLR_pre 0.01218219819.270361e-01
CRP_PRE SPLEEN_FDG_UPT_PRE_PET1 0.01102968139.339228e-01
WBC_PRE BMLR_pre -0.01081286449.352189e-01
BGL_PRE_PET1 BM_UPT_PRE_PET1 -0.00941975129.435506e-01
eGFR_PRE OVERALL_TIME -0.00889097039.467147e-01
SPLEEN_FDG_UPT_PRE_PET1 TIME_BETWEE_PET 0.00879148399.473101e-01
PLT_PRE SLR_pre -0.00844646439.493751e-01
CRP_PRE AST_PRE 0.00839390099.496898e-01
BW_PRE BMLR_pre -0.00808054249.515657e-01
HB_PRE SLR_pre 0.00762979449.542644e-01
HB_PRE BMLR_pre 0.00476475859.714291e-01
ALBUMIN_PRE ESTIMATED_SPLEE_VOL_PRE_PET1 0.00447257289.731805e-01
RBC_PRE BMLR_pre -0.00414986269.751150e-01
RBC_PRE AST_PRE 0.00412093939.752884e-01
LIVER_PRE_PET1 SLR_pre -0.00318587699.808944e-01
AST_PRE SLR_pre -0.00283501399.829982e-01
AGE HB_PRE -0.00241443499.855201e-01
PLT_PRE CRP_PRE -0.00238359909.857050e-01
HB_PRE ALT_PRE -0.00235321649.858872e-01
eGFR_PRE SLR_pre 0.00172441519.896580e-01
BMI SPLEEN_FDG_UPT_PRE_PET1 0.00166639889.900060e-01
HB_PRE OVERALL_TIME -0.00140410429.915790e-01
LDH_PRE BMI -0.00097924599.941270e-01
ESTIMATED_SPLEE_VOL_PRE_PET1OVERALL_TIME 0.00087732159.947383e-01
ALBUMIN_PRE eGFR_PRE 0.00055546629.966686e-01
ALBUMIN_PRE SPLEEN_FDG_UPT_PRE_PET1 0.00032168459.980707e-01

Descripcion variables cualitativas de entrada¶

Tabla de frecuencias de las variables cualitativas¶

In [1089]:
# Todas la tablas de frecuencias:
all_var <- univar_category(datos) 

all_var
$STATUS
A data.frame: 2 × 3
STATUSnrate
<fct><int><dbl>
ALIVE380.6440678
DEATH210.3559322
$GENDER
A data.frame: 2 × 3
GENDERnrate
<fct><int><dbl>
FEMALE200.3389831
MALE 390.6610169
$TNM_STAGE
A data.frame: 4 × 3
TNM_STAGEnrate
<fct><int><dbl>
I 60.1016949
II 150.2542373
III260.4406780
IV 120.2033898
$DIAGNOSTIC
A data.frame: 10 × 3
DIAGNOSTICnrate
<fct><int><dbl>
EWING SARCOMA 20.03389831
GASTRIC CANCER 30.05084746
GINECOLOGICAL 70.11864407
HEAD AND NECK 100.16949153
LUNG CANCER 190.32203390
MELANOMA 50.08474576
PANCREAS CANCER 70.11864407
RENAL CANCER 40.06779661
SARCOMA 10.01694915
UROTHELIAL CARCINOMA 10.01694915
$TREATMENT
A data.frame: 2 × 3
TREATMENTnrate
<fct><int><dbl>
CHEMO370.6271186
ICI 220.3728814
$ECOGPS
A data.frame: 5 × 3
ECOGPSnrate
<fct><int><dbl>
ASYMTOMATIC 260.44067797
BEDBOUND 10.01694915
SYMPTOMATIC >50 % IN THE BED 10.01694915
SYMPTOMATIC BUT AMBULATORY 170.28813559
SYMPTOMATIC,<50% IN BED DURING THE DAY140.23728814
$COMORBIDITIES
A data.frame: 6 × 3
COMORBIDITIESnrate
<fct><int><dbl>
ATEROESCLEROSIS 30.05084746
CANCER RRECURRENCE 80.13559322
CHRONIC INFLAMMATION 90.15254237
DIABETES MELLITUS 130.22033898
HYPERLIPIDEMIA 120.20338983
HYPERTENSION 140.23728814
$CTCNCI
A data.frame: 5 × 3
CTCNCInrate
<fct><int><dbl>
HIGHT SEVERE 40.06779661
MODERATE 110.18644068
NO SIDE EFFECTS 280.47457627
SEVERE 120.20338983
SLIGTHLY SIDE EFFCTS 40.06779661
$ACTION_TAKEN_
A data.frame: 7 × 3
ACTION_TAKEN_nrate
<fct><int><dbl>
ADDED OTHER TREATMEN 30.05084746
ADDED STEROIDS 10.01694915
DOSE NOT CHANGED 210.35593220
DOSE REDUCED 170.28813559
DRUG INTERRUPTED 90.15254237
DRUG WIHDRAWN 10.01694915
UNKNOW 70.11864407

Graficos de barras de las variables cualitativas¶

In [1090]:
# Diagrama de barras por separado:
plot(all_var)

Todos los diagramas de barra juntos¶

In [1091]:
# Todos los diagramas de barra juntos:
plot_bar_category(datos)

Tabla de frecuencias + diagrama de barras + diagrama de pastel de todas las cualitativas¶

In [1092]:
# Tabla de frecuencias + diagrama de barras + diagrama de sectores en una iteración:

vec_var_cual <- c('GENDER','TNM_STAGE','DIAGNOSTIC','TREATMENT','ECOGPS','COMORBIDITIES','CTCNCI','ACTION_TAKEN_')

for (i in vec_var_cual) {
    
    # 0. Seleccionar la variable con el contador i:
    #***************************************************************    
    var_i = i
    
    print("********************************************")
    print(var_i)
    print("********************************************")

    # 1. Creo la tabla de frecuencias relativas y absolutas:
    #***************************************************************

    # Tabla de frecuencias absolutas:
    tabla_frec = data.frame(t(table(datos[,var_i])))[,2:3]
    colnames(tabla_frec)<-c("Grupos","Frec_Absoluta")

    # Tabla de frecuencias relativas:
    tabla_frec$Frec_Relativa <- tabla_frec$Frec_Absoluta/sum(tabla_frec$Frec_Absoluta)
    print(tabla_frec)


    # 3. Crear el diagrama de sectores
    #***************************************************************
    # Create Data
    data <- data.frame(
      group=tabla_frec$Grupos,
      value=tabla_frec$Frec_Relativa
    )

    data <- data %>% 
      arrange(desc(group)) %>%
      mutate(prop = value / sum(data$value) *100) %>%
      mutate(ypos = cumsum(prop)- 0.5*prop )


    p2 <- ggplot(data, aes(x="", y=prop, fill=group)) +
      geom_bar(stat="identity", width=1, color="white") +
      coord_polar("y", start=0) +
      theme_void() + 
      theme(legend.position="none") +

      geom_text(aes(y = ypos, label = group), color = "white", size=6) +
      scale_fill_brewer(palette="Set1")

    
    
    # 2. Crear el barplot frecuencia Absoluta
    #***************************************************************
    p1 <- ggplot(data=tabla_frec, aes(x=Grupos, y=Frec_Absoluta)) +
      geom_bar(stat="identity", fill="steelblue")+
      geom_text(aes(label=Frec_Relativa), vjust=1.6, color="black", size=5)+
      theme_minimal()

   
      grid.arrange(p1, p2,
               nrow=2, ncol = 1,
               top = textGrob(var_i,gp=gpar(fontsize=16,font=3)))    
    
    
}
[1] "********************************************"
[1] "GENDER"
[1] "********************************************"
  Grupos Frec_Absoluta Frec_Relativa
1 FEMALE            20     0.3389831
2   MALE            39     0.6610169
[1] "********************************************"
[1] "TNM_STAGE"
[1] "********************************************"
  Grupos Frec_Absoluta Frec_Relativa
1      I             6     0.1016949
2     II            15     0.2542373
3    III            26     0.4406780
4     IV            12     0.2033898
[1] "********************************************"
[1] "DIAGNOSTIC"
[1] "********************************************"
                 Grupos Frec_Absoluta Frec_Relativa
1         EWING SARCOMA             2    0.03389831
2        GASTRIC CANCER             3    0.05084746
3         GINECOLOGICAL             7    0.11864407
4         HEAD AND NECK            10    0.16949153
5           LUNG CANCER            19    0.32203390
6              MELANOMA             5    0.08474576
7       PANCREAS CANCER             7    0.11864407
8          RENAL CANCER             4    0.06779661
9               SARCOMA             1    0.01694915
10 UROTHELIAL CARCINOMA             1    0.01694915
[1] "********************************************"
[1] "TREATMENT"
[1] "********************************************"
  Grupos Frec_Absoluta Frec_Relativa
1  CHEMO            37     0.6271186
2    ICI            22     0.3728814
[1] "********************************************"
[1] "ECOGPS"
[1] "********************************************"
                                  Grupos Frec_Absoluta Frec_Relativa
1                            ASYMTOMATIC            26    0.44067797
2                               BEDBOUND             1    0.01694915
3           SYMPTOMATIC >50 % IN THE BED             1    0.01694915
4             SYMPTOMATIC BUT AMBULATORY            17    0.28813559
5 SYMPTOMATIC,<50% IN BED DURING THE DAY            14    0.23728814
[1] "********************************************"
[1] "COMORBIDITIES"
[1] "********************************************"
                Grupos Frec_Absoluta Frec_Relativa
1      ATEROESCLEROSIS             3    0.05084746
2   CANCER RRECURRENCE             8    0.13559322
3 CHRONIC INFLAMMATION             9    0.15254237
4    DIABETES MELLITUS            13    0.22033898
5       HYPERLIPIDEMIA            12    0.20338983
6         HYPERTENSION            14    0.23728814
[1] "********************************************"
[1] "CTCNCI"
[1] "********************************************"
                Grupos Frec_Absoluta Frec_Relativa
1         HIGHT SEVERE             4    0.06779661
2             MODERATE            11    0.18644068
3      NO SIDE EFFECTS            28    0.47457627
4               SEVERE            12    0.20338983
5 SLIGTHLY SIDE EFFCTS             4    0.06779661
[1] "********************************************"
[1] "ACTION_TAKEN_"
[1] "********************************************"
                Grupos Frec_Absoluta Frec_Relativa
1 ADDED OTHER TREATMEN             3    0.05084746
2       ADDED STEROIDS             1    0.01694915
3     DOSE NOT CHANGED            21    0.35593220
4         DOSE REDUCED            17    0.28813559
5     DRUG INTERRUPTED             9    0.15254237
6        DRUG WIHDRAWN             1    0.01694915
7               UNKNOW             7    0.11864407

EXPLORACION MULTIVARIADA DEL DATASET¶

Variables numericas vs salida¶

Diagrama de dispersion por la salida¶

In [1093]:
# Diagrama de dispersion por pareja

options(repr.plot.width=8, repr.plot.height=5)
var_respuesta <- as.name(respuesta)

for (i in 1:length(var_num)) {
  name_i <- as.name(var_num[i])
  r <- list()
  
    r[[1]] <- ggplot(datos, aes(x=!!name_i, y=!!var_respuesta)) +
      geom_point() +
      geom_smooth(method=lm ,formula = y ~ x, color="red", fill="#69b3a2", se=TRUE)  +
    theme_minimal()
  
  grid.arrange(r[[1]],
               nrow=1, ncol = 1,
               top = textGrob(var_num[i],gp=gpar(fontsize=16,font=3)))
}

Orden de importancia de las variables cualitativas en el dataset¶

In [1094]:
# Mutual informacion variables cualitativas:
inf_gain <- information.gain(STATUS~., data = datos)
inf_gain <- inf_gain %>%
  arrange(desc(attr_importance))
inf_gain
A data.frame: 33 × 1
attr_importance
<dbl>
ACTION_TAKEN_1.327590e-01
DIAGNOSTIC1.265327e-01
TNM_STAGE8.123244e-02
ECOGPS5.316641e-02
COMORBIDITIES5.272323e-02
CTCNCI2.358443e-02
GENDER1.294311e-02
TREATMENT7.687645e-05
CYCLES_BETWEEN_PET1_PET20.000000e+00
AGE0.000000e+00
WBC_PRE0.000000e+00
RBC_PRE0.000000e+00
HB_PRE0.000000e+00
PLT_PRE0.000000e+00
CRP_PRE0.000000e+00
ALBUMIN_PRE0.000000e+00
LDH_PRE0.000000e+00
eGFR_PRE0.000000e+00
AST_PRE0.000000e+00
ALT_PRE0.000000e+00
K_PRE0.000000e+00
BGL_PRE_PET10.000000e+00
BMI0.000000e+00
BW_PRE0.000000e+00
SPLEEN_FDG_UPT_PRE_PET10.000000e+00
BM_UPT_PRE_PET10.000000e+00
LIVER_PRE_PET10.000000e+00
ESTIMATED_SPLEE_VOL_PRE_PET10.000000e+00
TIME_BETWEE_PET0.000000e+00
dias0.000000e+00
SLR_pre0.000000e+00
BMLR_pre0.000000e+00
OVERALL_TIME0.000000e+00

Estadisticos numericos variables numericos vs salida¶

In [1095]:
# 6. DESCRIPCION MULTIVARIADA: las variables en función de la respuesta
name_y <- as.name(respuesta[1])

# Descripciones de las variables cuantiativas vs salida
descripcion_num <- datos %>%
  group_by(!!name_y)%>%
  dlookr::describe()
descripcion_num
A tibble: 50 × 27
described_variablesSTATUSnnameansdse_meanIQRskewnesskurtosisp00p01p05p10p20p25p30p40p50p60p70p75p80p90p95p99p100
<chr><fct><int><int><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>
AGE ALIVE380 68.5526316 8.1562358 1.32311616 12.2500000-0.35796368-0.48367838 52.0000000 52.7400000 54.0000000 55.7000000 60.4000000 62.5000000 65.0000000 68.0000000 70.0000000 72.0000000 74.000000 74.750000 75.600000 76.300000 79.000000 83.410000 86.000000
AGE DEATH210 70.9047619 9.4228698 2.05623878 14.0000000-0.14495828-0.82993240 53.0000000 53.6000000 56.0000000 60.0000000 63.0000000 64.0000000 67.0000000 68.0000000 70.0000000 74.0000000 77.000000 78.000000 79.000000 83.000000 83.000000 86.200000 87.000000
ALBUMIN_PRE ALIVE380 3.7957895 0.5630834 0.09134418 0.5225000-1.11593692 1.04212745 2.1300000 2.3224000 2.7180000 2.9170000 3.3880000 3.6250000 3.7030000 3.8160000 3.9500000 3.9920000 4.100000 4.147500 4.226000 4.338000 4.479000 4.586700 4.620000
ALBUMIN_PRE DEATH210 3.8304762 0.5007642 0.10927570 0.5000000-1.80020192 5.06822812 2.1800000 2.3860000 3.2100000 3.4300000 3.6800000 3.6900000 3.7000000 3.7500000 3.8100000 3.9500000 4.180000 4.190000 4.190000 4.300000 4.350000 4.446000 4.470000
ALT_PRE ALIVE380 22.2657895 15.3574927 2.49131429 15.3500000 3.1506701114.40321439 3.7000000 4.8100000 7.8050000 9.8700000 13.7800000 14.0750000 14.5100000 15.9800000 17.6000000 20.8800000 27.810000 29.425000 29.800000 33.220000 36.775000 75.817000 96.500000
ALT_PRE DEATH210 35.3419048 60.648032713.23448575 12.8000000 3.7824564815.03863546 3.2000000 4.3600000 9.0000000 9.5000000 12.7000000 14.2000000 14.9000000 16.3000000 19.5000000 20.5000000 23.100000 27.000000 27.180000 34.800000114.800000248.160000281.500000
AST_PRE ALIVE380 24.4473684 18.2988810 2.96847049 7.6750000 4.5057774023.78498297 6.1000000 8.8380000 13.5850000 14.9600000 16.2000000 17.4750000 17.9000000 19.0200000 20.3000000 21.6400000 23.190000 25.150000 25.900000 34.420000 44.315000 95.239000123.100000
AST_PRE DEATH210 35.9290476 52.778073311.51711980 10.6000000 3.8406812515.65472906 11.9000000 12.1200000 13.0000000 13.0000000 13.7000000 14.9000000 15.7000000 21.1000000 21.9000000 23.4000000 24.600000 25.500000 27.800000 53.300000 94.200000220.600000252.200000
BGL_PRE_PET1 ALIVE380114.2105263 38.9498346 6.31849752 27.5000000 2.08022037 4.92887622 74.0000000 75.1100000 79.5500000 83.7000000 88.2000000 90.7500000 93.2000000 98.0000000100.0000000108.6000000115.600000118.250000126.000000167.100000184.000000239.430000262.000000
BGL_PRE_PET1 DEATH210106.6190476 27.7443259 6.05430826 15.0000000 1.96982468 4.59209992 75.0000000 76.2000000 81.0000000 83.0000000 93.0000000 94.0000000 94.0000000 95.0000000100.0000000101.0000000104.000000109.000000116.000000140.000000150.000000186.800000196.000000
BM_UPT_PRE_PET1 ALIVE380 2.4044737 0.4882993 0.07921261 0.5950000-0.10892825 0.16091481 1.3100000 1.3433000 1.5615000 1.8370000 2.0120000 2.1025000 2.2000000 2.3000000 2.4350000 2.5500000 2.636000 2.697500 2.768000 2.924000 3.216500 3.429700 3.500000
BM_UPT_PRE_PET1 DEATH210 2.3709524 0.4132905 0.09018738 0.5400000-0.06965851-0.47167636 1.5600000 1.5980000 1.7500000 1.8800000 2.0000000 2.0600000 2.1900000 2.3100000 2.4100000 2.4500000 2.530000 2.600000 2.650000 2.900000 3.040000 3.080000 3.090000
BMI ALIVE380 22.1357895 4.2896479 0.69587278 5.3525000 0.88974456 1.40256997 15.4300000 15.7297000 16.4355000 17.1050000 18.2240000 18.8325000 20.1230000 20.8080000 21.8250000 22.8320000 23.579000 24.185000 25.708000 26.914000 29.177500 33.497500 35.810000
BMI DEATH210 20.4747619 3.3661545 0.73455514 5.6200000-0.05915208-1.07758686 14.0800000 14.4980000 16.1700000 16.6800000 17.3000000 17.8900000 18.0700000 19.2600000 20.0700000 21.7900000 23.140000 23.510000 23.760000 24.460000 25.210000 25.666000 25.780000
BMLR_pre ALIVE380 0.9706065 0.2563308 0.04158235 0.2997767 1.15745006 3.55551554 0.4946996 0.5251850 0.6619485 0.6952792 0.7390075 0.7878715 0.8561207 0.9058442 0.9888729 1.0092731 1.069046 1.087648 1.126667 1.193460 1.290253 1.740426 1.895425
BMLR_pre DEATH210 1.0031606 0.2179211 0.04755429 0.2222760 1.45432156 2.49319478 0.7062500 0.7211562 0.7807808 0.7849829 0.8756614 0.8787879 0.8914956 0.9094488 0.9552239 0.9930314 1.062271 1.101064 1.115385 1.181373 1.458333 1.584596 1.616162
BW_PRE ALIVE380 57.2894737 12.6083153 2.04533883 15.7000000 0.55893823 0.76960150 33.5000000 35.6090000 39.8800000 41.4900000 46.0000000 48.5500000 50.1800000 54.8400000 57.4000000 59.1800000 63.470000 64.250000 67.220000 69.650000 78.275000 89.094000 94.200000
BW_PRE DEATH210 52.7561905 12.9480464 2.82549536 14.7000000-1.06771876 2.97454411 14.0800000 19.0840000 39.1000000 41.5000000 45.4000000 45.7000000 47.3000000 50.7000000 51.8000000 55.4000000 60.400000 60.400000 62.000000 65.900000 67.800000 74.200000 75.800000
CRP_PRE ALIVE380 1.1097368 1.9033392 0.30876238 0.5925000 2.03462033 3.33693226 0.0100000 0.0137000 0.0200000 0.0200000 0.0540000 0.0600000 0.0910000 0.1380000 0.1800000 0.3100000 0.599000 0.652500 1.718000 3.970000 4.983500 7.012100 7.260000
CRP_PRE DEATH210 1.8019048 4.1777011 0.91164911 0.5700000 3.6478751814.39621273 0.0200000 0.0200000 0.0200000 0.0400000 0.1800000 0.2000000 0.2100000 0.2600000 0.3500000 0.3900000 0.550000 0.770000 1.290000 3.870000 6.370000 16.178000 18.630000
CYCLES_BETWEEN_PET1_PET2 ALIVE380 7.3684211 8.8330493 1.43290981 3.7500000 3.2390467312.32718502 2.0000000 2.0000000 2.0000000 2.0000000 3.0000000 3.0000000 3.0000000 4.0000000 4.0000000 5.0000000 6.000000 6.750000 11.200000 14.000000 21.350000 40.970000 48.000000
CYCLES_BETWEEN_PET1_PET2 DEATH210 6.5238095 4.0201872 0.87727676 4.0000000 1.58544523 2.70180500 2.0000000 2.0000000 2.0000000 3.0000000 4.0000000 4.0000000 4.0000000 5.0000000 6.0000000 6.0000000 7.000000 8.000000 9.000000 10.000000 15.000000 17.400000 18.000000
dias ALIVE380 46.8421053137.893520722.36928289 23.7500000 4.0656318417.78192579 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 2.0000000 14.900000 23.750000 30.400000 64.200000319.650000597.360000735.000000
dias DEATH210 75.8095238127.562384427.83639439 86.0000000 1.95190402 3.08842106 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 3.0000000 7.0000000 33.0000000 52.000000 86.000000114.000000233.000000364.000000425.600000441.000000
eGFR_PRE ALIVE380 68.0105263 12.2979407 1.99498942 17.3000000 0.30544577-0.06003580 41.0000000 44.9590000 51.8700000 54.1900000 57.3800000 58.7750000 60.2000000 63.1000000 66.7000000 72.2200000 73.950000 76.075000 78.000000 81.610000 89.950000 95.586000 96.400000
eGFR_PRE DEATH210 76.9476190 35.4929235 7.74519089 26.9000000 2.8413178210.35403667 39.8000000 40.9800000 45.7000000 46.9000000 53.8000000 57.6000000 62.5000000 69.2000000 71.3000000 74.8000000 77.200000 84.500000 85.100000 96.600000114.000000191.280000210.600000
ESTIMATED_SPLEE_VOL_PRE_PET1ALIVE380143.3026316 61.1323619 9.91697862 72.8750000 0.98965582 0.68598500 50.8000000 53.8340000 76.0000000 81.7100000 96.5800000 98.2500000104.1100000114.7600000125.1500000145.4800000164.270000171.125000181.620000234.270000260.210000296.113000316.500000
ESTIMATED_SPLEE_VOL_PRE_PET1DEATH210149.1523810 54.430668011.87774554 57.0000000 0.90601407 1.04733786 56.9000000 63.6400000 90.6000000 92.5000000112.0000000116.0000000117.8000000138.6000000145.3000000148.4000000160.000000173.000000174.700000210.300000262.600000277.320000281.000000
HB_PRE ALIVE380 12.0042105 2.1079831 0.34196001 2.9500000-0.13880298-0.43535197 7.3000000 7.6700000 8.5210000 9.3300000 10.3200000 10.7000000 11.1300000 11.5800000 11.7500000 12.4200000 13.270000 13.650000 13.900000 14.680000 15.430000 15.663000 15.700000
HB_PRE DEATH210 12.5333333 2.0222100 0.44128240 2.2000000-0.52274310-0.02985859 8.2000000 8.3200000 8.8000000 9.6000000 11.3000000 11.6000000 12.0000000 12.2000000 12.7000000 13.1000000 13.400000 13.800000 14.600000 15.000000 15.000000 15.640000 15.800000
K_PRE ALIVE380 4.2657895 0.3521335 0.05712360 0.6025000 0.34365126-0.54862710 3.6900000 3.7233000 3.8140000 3.8300000 3.9200000 3.9400000 4.0220000 4.1240000 4.2550000 4.4060000 4.498000 4.542500 4.556000 4.690000 4.768000 5.046400 5.150000
K_PRE DEATH210 4.2395238 0.4226521 0.09223024 0.5400000-0.63083821 1.14258830 3.1100000 3.2380000 3.7500000 3.9000000 3.9700000 3.9700000 3.9800000 4.0900000 4.2800000 4.3700000 4.410000 4.510000 4.670000 4.710000 4.810000 4.906000 4.930000
LDH_PRE ALIVE380198.8105263100.746682016.34326993 43.2500000 2.68225454 7.28744364 77.8000000 84.5340000120.6500000132.1000000145.4000000157.2500000159.2000000174.0000000178.0000000184.0000000197.700000200.500000205.000000228.000000484.650000543.770000573.000000
LDH_PRE DEATH210231.8095238164.241169935.84036160 66.0000000 2.70732400 6.72154960111.0000000113.8000000125.0000000139.0000000142.0000000150.0000000151.0000000172.0000000204.0000000210.0000000214.000000216.000000221.000000253.000000670.000000734.800000751.000000
LIVER_PRE_PET1 ALIVE380 2.4084211 0.4367603 0.07085188 0.5875000 0.48581409 1.61826242 1.4900000 1.5048000 1.8275000 1.9710000 2.0220000 2.1100000 2.2240000 2.2880000 2.3600000 2.5120000 2.599000 2.697500 2.772000 2.839000 2.954000 3.524700 3.780000
LIVER_PRE_PET1 DEATH210 2.6519048 0.4027483 0.08788688 0.4200000 0.01403399-0.30052396 1.8900000 1.9280000 2.0800000 2.1100000 2.2500000 2.4400000 2.5100000 2.6000000 2.6900000 2.7300000 2.820000 2.860000 2.870000 3.200000 3.330000 3.394000 3.410000
OVERALL_TIME ALIVE380 17.7631579 16.2537883 2.63671263 16.5000000 1.49609275 1.96731456 1.0000000 1.0000000 1.8500000 2.7000000 4.4000000 6.0000000 7.2000000 11.0000000 14.5000000 15.2000000 20.000000 22.500000 25.800000 41.000000 51.950000 64.260000 65.000000
OVERALL_TIME DEATH210 21.5238095 15.1480000 3.30556461 17.0000000 1.30493144 1.91970514 1.0000000 1.6000000 4.0000000 8.0000000 12.0000000 12.0000000 13.0000000 16.0000000 17.0000000 21.0000000 23.000000 29.000000 30.000000 42.000000 47.000000 60.600000 64.000000
PLT_PRE ALIVE380253.0789474114.126383618.51374414130.2500000 1.24759679 1.21957508106.0000000111.5500000125.2500000142.8000000162.2000000176.5000000187.2000000206.0000000217.5000000244.6000000280.700000306.750000335.800000388.800000498.000000556.580000581.000000
PLT_PRE DEATH210219.4285714 93.120659120.32059376 84.0000000 1.42319105 2.24589115 78.0000000 88.8000000132.0000000140.0000000158.0000000163.0000000170.0000000193.0000000200.0000000210.0000000222.000000247.000000262.000000315.000000442.000000455.600000459.000000
RBC_PRE ALIVE380 4.0513158 0.6786440 0.11009059 0.7425000-0.46519364 0.69869643 2.2500000 2.3980000 2.8285000 3.2540000 3.6220000 3.6650000 3.7620000 4.0620000 4.1200000 4.3000000 4.358000 4.407500 4.422000 4.883000 5.164500 5.341200 5.430000
RBC_PRE DEATH210 4.1314286 0.6181366 0.13488846 0.6900000-0.54623453 0.36384720 2.7200000 2.7740000 2.9900000 3.3400000 3.8500000 3.8600000 3.9400000 3.9800000 4.2000000 4.2700000 4.460000 4.550000 4.690000 4.800000 4.950000 5.158000 5.210000
SLR_pre ALIVE380 0.9814276 0.1646818 0.02671492 0.2106722 0.49345198-0.18204133 0.7102473 0.7242998 0.7625899 0.7791925 0.8318182 0.8575311 0.8819217 0.9110861 0.9977876 1.0146910 1.045247 1.068203 1.087344 1.177965 1.330324 1.339409 1.339869
SLR_pre DEATH210 0.9524327 0.2066883 0.04510309 0.1964809 1.22516208 1.89503811 0.6486486 0.6718290 0.7645503 0.7684564 0.7806691 0.8035191 0.8062500 0.8717949 0.9166667 0.9722222 0.988189 1.000000 1.117647 1.166667 1.271795 1.484146 1.537234
SPLEEN_FDG_UPT_PRE_PET1 ALIVE380 2.3552632 0.2624596 0.04257658 0.2875000 0.25112213-0.71619335 1.9600000 1.9674000 1.9800000 2.0070000 2.0700000 2.1925000 2.2020000 2.2760000 2.3550000 2.4060000 2.477000 2.480000 2.618000 2.686000 2.779500 2.890000 2.890000
SPLEEN_FDG_UPT_PRE_PET1 DEATH210 2.3723810 0.2509363 0.05475880 0.3800000 0.66043156-0.94781786 2.1100000 2.1100000 2.1100000 2.1300000 2.1500000 2.1500000 2.1500000 2.2000000 2.2900000 2.4100000 2.510000 2.530000 2.580000 2.740000 2.810000 2.842000 2.850000
TIME_BETWEE_PET ALIVE380 10.5789474 8.9703162 1.45517743 11.5000000 1.40520467 1.58539181 1.0000000 1.3700000 2.0000000 2.7000000 4.0000000 4.0000000 5.0000000 6.0000000 7.5000000 9.0000000 11.800000 15.500000 18.200000 22.200000 26.200000 36.150000 38.000000
TIME_BETWEE_PET DEATH210 7.9047619 3.9484777 0.86162847 7.0000000-0.18573481-0.95771678 1.0000000 1.2000000 2.0000000 3.0000000 4.0000000 4.0000000 5.0000000 8.0000000 9.0000000 9.0000000 10.000000 11.000000 11.000000 12.000000 13.000000 14.600000 15.000000
WBC_PRE ALIVE380 6.0121053 2.6179496 0.42468751 2.9650000 0.39233540 0.44660813 1.5200000 1.5755000 1.7125000 2.3800000 4.1540000 4.4425000 4.5700000 5.3040000 5.9850000 6.6340000 7.253000 7.407500 7.790000 9.058000 9.960500 12.400900 13.300000
WBC_PRE DEATH210 5.7719048 2.5538023 0.55728535 3.2700000 1.16147740 2.53426202 1.5900000 1.9620000 3.4500000 3.4500000 3.7500000 3.9700000 4.0800000 4.6900000 5.1400000 6.5500000 7.080000 7.240000 7.430000 7.950000 8.660000 12.388000 13.320000

Graficos descriptivos variables numericas respecto a la salida¶

In [1096]:
options(repr.plot.width=16, repr.plot.height=8)


# boxplot + diagrama de error + histograma de densidad + violin plot
for (i in 1:length(var_num)) {
  name_i <- as.name(var_num[i])
  name_y <- as.name(respuesta[1])
  
  r <- list()
  
  # Histograma de densidad
  r[[1]]<-ggplot(datos, aes(x=!!name_i, fill=!!name_y)) +
    geom_density(alpha=0.4)+
    theme_light() +
    #theme(legend.position = "none") +
    xlab("") +
    ylab("Densidad de Frecuencia")
  
  # Boxplot
  r[[2]]<-ggplot(data=datos, aes(y=!!name_i, x=!!name_y)) +
    geom_boxplot(size = 0.4) +
    theme_light() +
    theme(legend.position = "none")+
    xlab("")
  
  # Violin plot con diagramas de error
  r[[3]]<-ggerrorplot(data = datos, x = respuesta, y = var_num[i], 
                      desc_stat = "mean_ci",
                      error.plot = "errorbar",
                      add = c("violin","mean"))+
    theme_light() +
    theme(legend.position = "none") +
    xlab("") +
    ylab("") +
    #ggtitle("Comparacion pvut") +
    stat_compare_means(comparisons = c("0","1"))+ # Add pairwise comparisons p-value
    stat_compare_means(label.y = 1.05*max(datos[,var_num[i]]))                  # Add global p-value
  
  grid.arrange(r[[1]], r[[2]], r[[3]],
               nrow=3, ncol = 1,
               top = textGrob(var_num[i],gp=gpar(fontsize=16,font=3)))
}

Variables cualitativas vs salida¶

Todos los graficos de barras de las variables cualitativas segun la salida¶

In [1098]:
# Todos los graficos de barras de cualitativas vs respuesta
name_y <- as.name(respuesta[1])

datos %>%
  group_by(!!name_y) %>%
  plot_bar_category()

Graficos descriptivos variables cualitativas vs salida(Mosaic plot + Chi cuadrado)¶

In [1099]:
# Mosaic plot y chi cuadrado:
r <- list()
for (i in 1:length(var_cual)) {
    # Variable de salida o respuesta
    name_y <- as.name(respuesta[1])

    # Variable de entrada
    name_i <- as.name(var_cual[i])


    # Tabla de contingencias de una variable con la target
    categ <- target_by(datos, !!name_y)
    cat_cat <- relate(categ, !!name_i)
    r[[i]]<-as.data.frame.matrix(cat_cat)
    
    # Chi cuadrado
    print(summary(cat_cat))
    
    # Mosaico de una variable con la target
    grid.arrange(plot(cat_cat),nrow=1, ncol = 1)
    
}
r
Call: xtabs(formula = formula_str, data = data, addNA = TRUE)
Number of cases in table: 59 
Number of factors: 2 
Test for independence of all factors:
	Chisq = 1.4811, df = 1, p-value = 0.2236
Call: xtabs(formula = formula_str, data = data, addNA = TRUE)
Number of cases in table: 59 
Number of factors: 2 
Test for independence of all factors:
	Chisq = 9.495, df = 3, p-value = 0.02338
	Chi-squared approximation may be incorrect
Call: xtabs(formula = formula_str, data = data, addNA = TRUE)
Number of cases in table: 59 
Number of factors: 2 
Test for independence of all factors:
	Chisq = 12.737, df = 9, p-value = 0.1749
	Chi-squared approximation may be incorrect
Call: xtabs(formula = formula_str, data = data, addNA = TRUE)
Number of cases in table: 59 
Number of factors: 2 
Test for independence of all factors:
	Chisq = 0.009083, df = 1, p-value = 0.9241
Call: xtabs(formula = formula_str, data = data, addNA = TRUE)
Number of cases in table: 59 
Number of factors: 2 
Test for independence of all factors:
	Chisq = 5.748, df = 4, p-value = 0.2188
	Chi-squared approximation may be incorrect
Call: xtabs(formula = formula_str, data = data, addNA = TRUE)
Number of cases in table: 59 
Number of factors: 2 
Test for independence of all factors:
	Chisq = 5.938, df = 5, p-value = 0.3123
	Chi-squared approximation may be incorrect
Call: xtabs(formula = formula_str, data = data, addNA = TRUE)
Number of cases in table: 59 
Number of factors: 2 
Test for independence of all factors:
	Chisq = 2.7546, df = 4, p-value = 0.5997
	Chi-squared approximation may be incorrect
Call: xtabs(formula = formula_str, data = data, addNA = TRUE)
Number of cases in table: 59 
Number of factors: 2 
Test for independence of all factors:
	Chisq = 13.937, df = 6, p-value = 0.03035
	Chi-squared approximation may be incorrect
  1. A data.frame: 2 × 2
    FEMALEMALE
    <int><int>
    ALIVE1523
    DEATH 516
  2. A data.frame: 2 × 4
    IIIIIIIV
    <int><int><int><int>
    ALIVE181910
    DEATH57 7 2
  3. A data.frame: 2 × 10
    EWING SARCOMAGASTRIC CANCERGINECOLOGICALHEAD AND NECKLUNG CANCERMELANOMAPANCREAS CANCERRENAL CANCERSARCOMAUROTHELIAL CARCINOMA
    <int><int><int><int><int><int><int><int><int><int>
    ALIVE20661424301
    DEATH0314 533110
  4. A data.frame: 2 × 2
    CHEMOICI
    <int><int>
    ALIVE2414
    DEATH13 8
  5. A data.frame: 2 × 5
    ASYMTOMATICBEDBOUNDSYMPTOMATIC >50 % IN THE BEDSYMPTOMATIC BUT AMBULATORYSYMPTOMATIC,<50% IN BED DURING THE DAY
    <int><int><int><int><int>
    ALIVE1901810
    DEATH 7109 4
  6. A data.frame: 2 × 6
    ATEROESCLEROSISCANCER RRECURRENCECHRONIC INFLAMMATIONDIABETES MELLITUSHYPERLIPIDEMIAHYPERTENSION
    <int><int><int><int><int><int>
    ALIVE2359712
    DEATH15445 2
  7. A data.frame: 2 × 5
    HIGHT SEVEREMODERATENO SIDE EFFECTSSEVERESLIGTHLY SIDE EFFCTS
    <int><int><int><int><int>
    ALIVE262172
    DEATH25 752
  8. A data.frame: 2 × 7
    ADDED OTHER TREATMENADDED STEROIDSDOSE NOT CHANGEDDOSE REDUCEDDRUG INTERRUPTEDDRUG WIHDRAWNUNKNOW
    <int><int><int><int><int><int><int>
    ALIVE30179306
    DEATH01 48611

DEFINIMOS LOS DATAFRAMES DE ENTRADA Y SALIDA¶

In [97]:
# Voy a definir el data frame de entrada: X
X = datos[-1]  
head(X)

# Voy a definir la variable de salida:
y = as.factor(as.vector(unlist(datos[,respuesta])))
head(y)
A data.frame: 6 × 33
CYCLES_BETWEEN_PET1_PET2GENDERAGETNM_STAGEWBC_PRERBC_PREHB_PREPLT_PRECRP_PREALBUMIN_PRELDH_PREeGFR_PREAST_PREALT_PREK_PREBGL_PRE_PET1BMIBW_PRESPLEEN_FDG_UPT_PRE_PET1BM_UPT_PRE_PET1LIVER_PRE_PET1ESTIMATED_SPLEE_VOL_PRE_PET1DIAGNOSTICTREATMENTECOGPSCOMORBIDITIESCTCNCIACTION_TAKEN_TIME_BETWEE_PETdiasSLR_preBMLR_preOVERALL_TIME
<dbl><fct><dbl><fct><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><fct><fct><fct><fct><fct><fct><dbl><dbl><dbl><dbl><dbl>
13MALE 54III 3.823.03 9.701463.672.13573.060.116.8 6.74.87 9322.9066.52.672.862.45316.5GINECOLOGICALICISYMPTOMATIC BUT AMBULATORY HYPERLIPIDEMIA SEVERE DRUG INTERRUPTED2 250.85869571.0144928 2
22FEMALE79IV 10.873.9311.505154.112.65158.057.313.5 8.03.9210816.4733.52.402.702.70102.3HEAD AND NECKICIASYMTOMATIC HYPERTENSION NO SIDE EFFECTSDRUG INTERRUPTED4 01.00995020.930348350
33MALE 60III 8.404.41 8.563550.094.32 77.882.119.113.74.56 8720.1558.12.571.312.27 81.5LUNG CANCER ICISYMPTOMATIC,<50% IN BED DURING THE DAYDIABETES MELLITUS MODERATE DOSE REDUCED 4 140.99557521.0884956 2
43MALE 76II 4.663.5511.701780.103.70195.065.717.715.33.8313522.8356.72.252.462.26224.4RENAL CANCER ICIASYMTOMATIC HYPERTENSION NO SIDE EFFECTSDOSE NOT CHANGED6 00.88114750.717213112
55FEMALE70II 5.865.1915.103437.263.94166.079.918.110.23.8311118.2941.72.482.522.55260.0MELANOMA ICISYMPTOMATIC BUT AMBULATORY DIABETES MELLITUS NO SIDE EFFECTSDOSE NOT CHANGED6 00.82692311.019230812
64MALE 54IV 2.954.4514.501670.254.16135.076.314.414.04.55 9827.1880.42.011.402.83198.4LUNG CANCER ICIASYMTOMATIC CANCER RRECURRENCEHIGHT SEVERE DOSE NOT CHANGED63631.13215860.577092518
  1. ALIVE
  2. ALIVE
  3. ALIVE
  4. ALIVE
  5. ALIVE
  6. ALIVE
Levels:
  1. 'ALIVE'
  2. 'DEATH'

FEATURE SELECTION (SELECCION DE CARACTERISTICAS)¶

  1. Feature selection univariada ( ANOVA/Kruskall Wallis = Cuantitativa vs salida o Chi Cuadrado = Cualitativas vs salida, MI = Mutual information)
  • Quitar las variables con MI = 0
  • Quitar las variables con p-valores >25% = 0.25
  1. Feature selection multivariado:
  • Modelo logístico (caso binario) mejor modelo según el BIC. --> con pocos datos y pocas variables

Feature selection univariado: cuantitativas vs la salida¶

Wilcoxon rank test¶

Wilcoxon rank sum test es para comparar dos medianas. Casos donde la variable de salida es binaria.

In [98]:
## Multiple Wilcoxon rank sum tests - casos binarios

Wilk_p <- as.data.frame(t(as.data.frame(lapply(datos[,var_num], function(x) wilcox.test(x ~ as.numeric(datos$STATUS))$p.value)))) %>%
  arrange(V1)
                                               
Wilk_p
A data.frame: 25 × 1
V1
<dbl>
LIVER_PRE_PET10.03661853
dias0.10842987
OVERALL_TIME0.16811048
BMI0.20532924
CYCLES_BETWEEN_PET1_PET20.25040192
PLT_PRE0.28165920
HB_PRE0.28510675
CRP_PRE0.29208464
SLR_pre0.29241632
BW_PRE0.35436014
AGE0.36628834
LDH_PRE0.36672313
eGFR_PRE0.47616981
ESTIMATED_SPLEE_VOL_PRE_PET10.47620848
WBC_PRE0.59038639
BGL_PRE_PET10.60106294
RBC_PRE0.68642375
BMLR_pre0.70397948
BM_UPT_PRE_PET10.73352128
TIME_BETWEE_PET0.74493899
AST_PRE0.84311765
SPLEEN_FDG_UPT_PRE_PET10.86792438
ALT_PRE0.91803659
K_PRE0.95580153
ALBUMIN_PRE0.96841944

kruskall wallis test¶

In [99]:
## Kruskall wallis rank sum tests - todos los casos
Kruskal_p <- as.data.frame(t(as.data.frame(lapply(datos[,var_num], function(x) kruskal.test(x ~ as.numeric(datos$STATUS))$p.value)))) %>%
  arrange(V1)
                                               
Kruskal_p
A data.frame: 25 × 1
V1
<dbl>
LIVER_PRE_PET10.03591323
dias0.10657425
OVERALL_TIME0.16567915
BMI0.20251128
CYCLES_BETWEEN_PET1_PET20.24712716
PLT_PRE0.27813620
HB_PRE0.28155371
CRP_PRE0.28847137
SLR_pre0.28879571
BW_PRE0.35026204
AGE0.36209905
LDH_PRE0.36253452
eGFR_PRE0.47128317
ESTIMATED_SPLEE_VOL_PRE_PET10.47132205
WBC_PRE0.58493396
BGL_PRE_PET10.59556093
RBC_PRE0.68061139
BMLR_pre0.69811238
BM_UPT_PRE_PET10.72756794
TIME_BETWEE_PET0.73894218
AST_PRE0.83692878
SPLEEN_FDG_UPT_PRE_PET10.86169699
ALT_PRE0.91175656
K_PRE0.94949537
ALBUMIN_PRE0.96210768

Anova¶

Ahora hacemos la ANOVA que es el equivalente paramétrico a Kurskall Wallis

In [100]:
## ANOVA - para todos los casos
ANOVA_p<-as.data.frame(t(as.data.frame(lapply(datos[,var_num], function(x) anova(lm(x ~ datos$STATUS))$`Pr(>F)` [1])))) %>%
  arrange(V1)
                                              
ANOVA_p
A data.frame: 25 × 1
V1
<dbl>
LIVER_PRE_PET10.03959446
BMI0.13130022
eGFR_PRE0.16275397
BW_PRE0.19551371
TIME_BETWEE_PET0.20064551
ALT_PRE0.21078341
AST_PRE0.22687045
PLT_PRE0.25324994
AGE0.31995232
LDH_PRE0.34219043
HB_PRE0.35305717
CRP_PRE0.38557634
OVERALL_TIME0.38727919
dias0.43112427
BGL_PRE_PET10.43388029
SLR_pre0.55708977
BMLR_pre0.62489716
RBC_PRE0.65604217
CYCLES_BETWEEN_PET1_PET20.68048920
ESTIMATED_SPLEE_VOL_PRE_PET10.71612406
WBC_PRE0.73485464
BM_UPT_PRE_PET10.79115621
K_PRE0.79941543
SPLEEN_FDG_UPT_PRE_PET10.80844654
ALBUMIN_PRE0.81478133

Feature selection univariado: cualitativa vs la salida¶

Chi cuadrado de Pearson¶

In [101]:
res_sig <- rep(0,length(var_cual))

for (i in 1:length(var_cual)){
  chisq <- chisq.test(table(as.factor(as.character(datos[,var_cual[i]])),datos[,'STATUS']),correct = TRUE,simulate.p.value = 10000)
  res_sig[i]<-chisq$p.value
}
df_sig <- data.frame(p_valor=res_sig)
row.names(df_sig) <- colnames(datos[,var_cual])

idx_sig <- sort(df_sig$p_valor,decreasing = FALSE,index.return=TRUE)$ix
df_sig_sort <- data.frame(p_valor=res_sig[idx_sig])
row.names(df_sig_sort) <- colnames(datos[,var_cual])[idx_sig]
df_sig_sort
A data.frame: 8 × 1
p_valor
<dbl>
ACTION_TAKEN_0.01399300
TNM_STAGE0.02348826
DIAGNOSTIC0.14492754
ECOGPS0.18440780
GENDER0.25187406
COMORBIDITIES0.31534233
CTCNCI0.67266367
TREATMENT1.00000000

Filtro univariado Mutual information (MI)¶

In [37]:
# Mutual informacion variables cuantitativas:
inf_gain <- information.gain(STATUS~., data = datos)
inf_gain <- inf_gain %>%
  arrange(desc(attr_importance))
inf_gain
A data.frame: 33 × 1
attr_importance
<dbl>
ACTION_TAKEN_1.327590e-01
DIAGNOSTIC1.265327e-01
TNM_STAGE8.123244e-02
ECOGPS5.316641e-02
COMORBIDITIES5.272323e-02
CTCNCI2.358443e-02
GENDER1.294311e-02
TREATMENT7.687645e-05
CYCLES_BETWEEN_PET1_PET20.000000e+00
AGE0.000000e+00
WBC_PRE0.000000e+00
RBC_PRE0.000000e+00
HB_PRE0.000000e+00
PLT_PRE0.000000e+00
CRP_PRE0.000000e+00
ALBUMIN_PRE0.000000e+00
LDH_PRE0.000000e+00
eGFR_PRE0.000000e+00
AST_PRE0.000000e+00
ALT_PRE0.000000e+00
K_PRE0.000000e+00
BGL_PRE_PET10.000000e+00
BMI0.000000e+00
BW_PRE0.000000e+00
SPLEEN_FDG_UPT_PRE_PET10.000000e+00
BM_UPT_PRE_PET10.000000e+00
LIVER_PRE_PET10.000000e+00
ESTIMATED_SPLEE_VOL_PRE_PET10.000000e+00
TIME_BETWEE_PET0.000000e+00
dias0.000000e+00
SLR_pre0.000000e+00
BMLR_pre0.000000e+00
OVERALL_TIME0.000000e+00

Feature selection multivariado - BIC modelo estadístico - BINARIO¶

In [1117]:
# como elegia muchas variables fallaba(necesita menos variables),por eso ponemos a mano las que queremos(se puede jugar intercambiando variables)
# calcular los 5 modelos segun el BIC
modelos_5top <-
  glmulti(STATUS~ACTION_TAKEN_+TNM_STAGE+DIAGNOSTIC+LIVER_PRE_PET1+dias+OVERALL_TIME+BMI+CYCLES_BETWEEN_PET1_PET2+SPLEEN_FDG_UPT_PRE_PET1+BM_UPT_PRE_PET1+ESTIMATED_SPLEE_VOL_PRE_PET1+SLR_pre+BMLR_pre, data = datos,
          level = 1,               # No interaction considered
          method = "h",            # Exhaustive approach
          crit = "bic",            # BIC as criteria
          confsetsize = 5,         # Keep 5 best models
          plotty = F, report = F,  # No plot or interim reports
          fitfunction = "glm",     # glm function
          family = binomial)       # binomial family for logistic regression

# Los 5 mejores modelos
print(modelos_5top@formulas)
options(warn = -1)
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Warning message:
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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Warning message:
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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Warning message:
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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Warning message:
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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Warning message:
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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Warning message:
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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Warning message:
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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"glm.fit: fitted probabilities numerically 0 or 1 occurred"
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Warning message in glmulti(y = "STATUS", data = datos, level = 1, method = "h", :
"!fitting function failed to converge. Model skipped: STATUS~1+ ACTION_TAKEN_+ TNM_STAGE+ DIAGNOSTIC+ dias+ CYCLES_BETWEEN_PET1_PET2+ SPLEEN_FDG_UPT_PRE_PET1+ BM_UPT_PRE_PET1+ ESTIMATED_SPLEE_VOL_PRE_PET1+ SLR_pre+ BMLR_pre!fitting function failed to converge. Model skipped: STATUS~1+ ACTION_TAKEN_+ TNM_STAGE+ DIAGNOSTIC+ LIVER_PRE_PET1+ dias+ CYCLES_BETWEEN_PET1_PET2+ SPLEEN_FDG_UPT_PRE_PET1+ BM_UPT_PRE_PET1+ ESTIMATED_SPLEE_VOL_PRE_PET1+ SLR_pre+ BMLR_pre!fitting function failed to converge. Model skipped: STATUS~1+ ACTION_TAKEN_+ TNM_STAGE+ DIAGNOSTIC+ OVERALL_TIME+ CYCLES_BETWEEN_PET1_PET2+ SPLEEN_FDG_UPT_PRE_PET1+ BM_UPT_PRE_PET1+ ESTIMATED_SPLEE_VOL_PRE_PET1+ SLR_pre+ BMLR_pre!fitting function failed to converge. Model skipped: STATUS~1+ ACTION_TAKEN_+ TNM_STAGE+ DIAGNOSTIC+ LIVER_PRE_PET1+ OVERALL_TIME+ CYCLES_BETWEEN_PET1_PET2+ SPLEEN_FDG_UPT_PRE_PET1+ BM_UPT_PRE_PET1+ ESTIMATED_SPLEE_VOL_PRE_PET1+ SLR_pre+ BMLR_pre!fitting function failed to converge. Model skipped: STATUS~1+ ACTION_TAKEN_+ TNM_STAGE+ DIAGNOSTIC+ dias+ OVERALL_TIME+ CYCLES_BETWEEN_PET1_PET2+ SPLEEN_FDG_UPT_PRE_PET1+ BM_UPT_PRE_PET1+ ESTIMATED_SPLEE_VOL_PRE_PET1+ SLR_pre+ BMLR_pre!fitting function failed to converge. Model skipped: STATUS~1+ ACTION_TAKEN_+ TNM_STAGE+ DIAGNOSTIC+ LIVER_PRE_PET1+ dias+ OVERALL_TIME+ CYCLES_BETWEEN_PET1_PET2+ SPLEEN_FDG_UPT_PRE_PET1+ BM_UPT_PRE_PET1+ ESTIMATED_SPLEE_VOL_PRE_PET1+ SLR_pre+ BMLR_pre!fitting function failed to converge. Model skipped: STATUS~1+ ACTION_TAKEN_+ TNM_STAGE+ DIAGNOSTIC+ BMI+ CYCLES_BETWEEN_PET1_PET2+ SPLEEN_FDG_UPT_PRE_PET1+ BM_UPT_PRE_PET1+ ESTIMATED_SPLEE_VOL_PRE_PET1+ SLR_pre+ BMLR_pre!fitting function failed to converge. Model skipped: STATUS~1+ ACTION_TAKEN_+ TNM_STAGE+ DIAGNOSTIC+ LIVER_PRE_PET1+ BMI+ CYCLES_BETWEEN_PET1_PET2+ SPLEEN_FDG_UPT_PRE_PET1+ BM_UPT_PRE_PET1+ ESTIMATED_SPLEE_VOL_PRE_PET1+ SLR_pre+ BMLR_pre!fitting function failed to converge. Model skipped: STATUS~1+ ACTION_TAKEN_+ TNM_STAGE+ DIAGNOSTIC+ dias+ BMI+ CYCLES_BETWEEN_PET1_PET2+ SPLEEN_FDG_UPT_PRE_PET1+ BM_UPT_PRE_PET1+ ESTIMATED_SPLEE_VOL_PRE_PET1+ SLR_pre+ BMLR_pre!fitting function failed to converge. Model skipped: STATUS~1+ ACTION_TAKEN_+ TNM_STAGE+ DIAGNOSTIC+ LIVER_PRE_PET1+ dias+ BMI+ CYCLES_BETWEEN_PET1_PET2+ SPLEEN_FDG_UPT_PRE_PET1+ BM_UPT_PRE_PET1+ ESTIMATED_SPLEE_VOL_PRE_PET1+ SLR_pre+ BMLR_pre!fitting function failed to converge. Model skipped: STATUS~1+ ACTION_TAKEN_+ TNM_STAGE+ DIAGNOSTIC+ OVERALL_TIME+ BMI+ CYCLES_BETWEEN_PET1_PET2+ SPLEEN_FDG_UPT_PRE_PET1+ BM_UPT_PRE_PET1+ ESTIMATED_SPLEE_VOL_PRE_PET1+ SLR_pre+ BMLR_pre!fitting function failed to converge. Model skipped: STATUS~1+ ACTION_TAKEN_+ TNM_STAGE+ DIAGNOSTIC+ LIVER_PRE_PET1+ OVERALL_TIME+ BMI+ CYCLES_BETWEEN_PET1_PET2+ SPLEEN_FDG_UPT_PRE_PET1+ BM_UPT_PRE_PET1+ ESTIMATED_SPLEE_VOL_PRE_PET1+ SLR_pre+ BMLR_pre!fitting function failed to converge. Model skipped: STATUS~1+ ACTION_TAKEN_+ TNM_STAGE+ DIAGNOSTIC+ dias+ OVERALL_TIME+ BMI+ CYCLES_BETWEEN_PET1_PET2+ SPLEEN_FDG_UPT_PRE_PET1+ BM_UPT_PRE_PET1+ ESTIMATED_SPLEE_VOL_PRE_PET1+ SLR_pre+ BMLR_pre!fitting function failed to converge. Model skipped: STATUS~1+ ACTION_TAKEN_+ TNM_STAGE+ DIAGNOSTIC+ LIVER_PRE_PET1+ dias+ OVERALL_TIME+ BMI+ CYCLES_BETWEEN_PET1_PET2+ SPLEEN_FDG_UPT_PRE_PET1+ BM_UPT_PRE_PET1+ ESTIMATED_SPLEE_VOL_PRE_PET1+ SLR_pre+ BMLR_pre!fitting function failed to converge. Model skipped: NA!fitting function failed to converge. Model skipped: NA!fitting function failed to converge. Model skipped: NA!fitting function failed to converge. Model skipped: NA!fitting function failed to converge. Model skipped: NA!fitting function failed to converge. Model skipped: NA!fitting function failed to converge. Model skipped: NA!fitting function failed to converge. Model skipped: NA!fitting function failed to converge. Model skipped: NA!fitting function failed to converge. Model skipped: NA!fitting function failed to converge. Model skipped: NA!fitting function failed to converge. Model skipped: NA!fitting function failed to converge. Model skipped: NA!fitting function failed to converge. Model skipped: NA!fitting function failed to converge. Model skipped: NA!fitting function failed to converge. Model skipped: NA"
Warning message in glmulti(y = "STATUS", data = datos, level = 1, method = "h", :
"!fitting function failed to converge. Model skipped: STATUS~1+ ACTION_TAKEN_+ TNM_STAGE+ DIAGNOSTIC+ dias+ CYCLES_BETWEEN_PET1_PET2+ SPLEEN_FDG_UPT_PRE_PET1+ BM_UPT_PRE_PET1+ ESTIMATED_SPLEE_VOL_PRE_PET1+ SLR_pre+ BMLR_pre!fitting function failed to converge. Model skipped: STATUS~1+ ACTION_TAKEN_+ TNM_STAGE+ DIAGNOSTIC+ LIVER_PRE_PET1+ dias+ CYCLES_BETWEEN_PET1_PET2+ SPLEEN_FDG_UPT_PRE_PET1+ BM_UPT_PRE_PET1+ ESTIMATED_SPLEE_VOL_PRE_PET1+ SLR_pre+ BMLR_pre!fitting function failed to converge. Model skipped: STATUS~1+ ACTION_TAKEN_+ TNM_STAGE+ DIAGNOSTIC+ OVERALL_TIME+ CYCLES_BETWEEN_PET1_PET2+ SPLEEN_FDG_UPT_PRE_PET1+ BM_UPT_PRE_PET1+ ESTIMATED_SPLEE_VOL_PRE_PET1+ SLR_pre+ BMLR_pre!fitting function failed to converge. Model skipped: STATUS~1+ ACTION_TAKEN_+ TNM_STAGE+ DIAGNOSTIC+ LIVER_PRE_PET1+ OVERALL_TIME+ CYCLES_BETWEEN_PET1_PET2+ SPLEEN_FDG_UPT_PRE_PET1+ BM_UPT_PRE_PET1+ ESTIMATED_SPLEE_VOL_PRE_PET1+ SLR_pre+ BMLR_pre!fitting function failed to converge. Model skipped: STATUS~1+ ACTION_TAKEN_+ TNM_STAGE+ DIAGNOSTIC+ dias+ OVERALL_TIME+ CYCLES_BETWEEN_PET1_PET2+ SPLEEN_FDG_UPT_PRE_PET1+ BM_UPT_PRE_PET1+ ESTIMATED_SPLEE_VOL_PRE_PET1+ SLR_pre+ BMLR_pre!fitting function failed to converge. Model skipped: STATUS~1+ ACTION_TAKEN_+ TNM_STAGE+ DIAGNOSTIC+ LIVER_PRE_PET1+ dias+ OVERALL_TIME+ CYCLES_BETWEEN_PET1_PET2+ SPLEEN_FDG_UPT_PRE_PET1+ BM_UPT_PRE_PET1+ ESTIMATED_SPLEE_VOL_PRE_PET1+ SLR_pre+ BMLR_pre!fitting function failed to converge. Model skipped: STATUS~1+ ACTION_TAKEN_+ TNM_STAGE+ DIAGNOSTIC+ BMI+ CYCLES_BETWEEN_PET1_PET2+ SPLEEN_FDG_UPT_PRE_PET1+ BM_UPT_PRE_PET1+ ESTIMATED_SPLEE_VOL_PRE_PET1+ SLR_pre+ BMLR_pre!fitting function failed to converge. Model skipped: STATUS~1+ ACTION_TAKEN_+ TNM_STAGE+ DIAGNOSTIC+ LIVER_PRE_PET1+ BMI+ CYCLES_BETWEEN_PET1_PET2+ SPLEEN_FDG_UPT_PRE_PET1+ BM_UPT_PRE_PET1+ ESTIMATED_SPLEE_VOL_PRE_PET1+ SLR_pre+ BMLR_pre!fitting function failed to converge. Model skipped: STATUS~1+ ACTION_TAKEN_+ TNM_STAGE+ DIAGNOSTIC+ dias+ BMI+ CYCLES_BETWEEN_PET1_PET2+ SPLEEN_FDG_UPT_PRE_PET1+ BM_UPT_PRE_PET1+ ESTIMATED_SPLEE_VOL_PRE_PET1+ SLR_pre+ BMLR_pre!fitting function failed to converge. Model skipped: STATUS~1+ ACTION_TAKEN_+ TNM_STAGE+ DIAGNOSTIC+ LIVER_PRE_PET1+ dias+ BMI+ CYCLES_BETWEEN_PET1_PET2+ SPLEEN_FDG_UPT_PRE_PET1+ BM_UPT_PRE_PET1+ ESTIMATED_SPLEE_VOL_PRE_PET1+ SLR_pre+ BMLR_pre!fitting function failed to converge. Model skipped: STATUS~1+ ACTION_TAKEN_+ TNM_STAGE+ DIAGNOSTIC+ OVERALL_TIME+ BMI+ CYCLES_BETWEEN_PET1_PET2+ SPLEEN_FDG_UPT_PRE_PET1+ BM_UPT_PRE_PET1+ ESTIMATED_SPLEE_VOL_PRE_PET1+ SLR_pre+ BMLR_pre!fitting function failed to converge. Model skipped: STATUS~1+ ACTION_TAKEN_+ TNM_STAGE+ DIAGNOSTIC+ LIVER_PRE_PET1+ OVERALL_TIME+ BMI+ CYCLES_BETWEEN_PET1_PET2+ SPLEEN_FDG_UPT_PRE_PET1+ BM_UPT_PRE_PET1+ ESTIMATED_SPLEE_VOL_PRE_PET1+ SLR_pre+ BMLR_pre!fitting function failed to converge. Model skipped: STATUS~1+ ACTION_TAKEN_+ TNM_STAGE+ DIAGNOSTIC+ dias+ OVERALL_TIME+ BMI+ CYCLES_BETWEEN_PET1_PET2+ SPLEEN_FDG_UPT_PRE_PET1+ BM_UPT_PRE_PET1+ ESTIMATED_SPLEE_VOL_PRE_PET1+ SLR_pre+ BMLR_pre!fitting function failed to converge. Model skipped: STATUS~1+ ACTION_TAKEN_+ TNM_STAGE+ DIAGNOSTIC+ LIVER_PRE_PET1+ dias+ OVERALL_TIME+ BMI+ CYCLES_BETWEEN_PET1_PET2+ SPLEEN_FDG_UPT_PRE_PET1+ BM_UPT_PRE_PET1+ ESTIMATED_SPLEE_VOL_PRE_PET1+ SLR_pre+ BMLR_pre!fitting function failed to converge. Model skipped: NA!fitting function failed to converge. Model skipped: NA!fitting function failed to converge. Model skipped: NA!fitting function failed to converge. Model skipped: NA!fitting function failed to converge. Model skipped: NA!fitting function failed to converge. Model skipped: NA!fitting function failed to converge. Model skipped: NA!fitting function failed to converge. Model skipped: NA!fitting function failed to converge. Model skipped: NA!fitting function failed to converge. Model skipped: NA!fitting function failed to converge. Model skipped: NA!fitting function failed to converge. Model skipped: NA!fitting function failed to converge. Model skipped: NA!fitting function failed to converge. Model skipped: NA!fitting function failed to converge. Model skipped: NA!fitting function failed to converge. Model skipped: NA"
[[1]]
STATUS ~ 1 + LIVER_PRE_PET1 + BMI
<environment: 0x0000024ae70aa830>

[[2]]
STATUS ~ 1 + LIVER_PRE_PET1
<environment: 0x0000024ae70aa830>

[[3]]
STATUS ~ 1 + LIVER_PRE_PET1 + dias + BMI
<environment: 0x0000024ae70aa830>

[[4]]
STATUS ~ 1
<environment: 0x0000024ae70aa830>

[[5]]
STATUS ~ 1 + TNM_STAGE + LIVER_PRE_PET1 + BMI
<environment: 0x0000024ae70aa830>

In [1116]:
# El summary de los 5 mejores modelos
summary(modelos_5top@objects[[5]])
Call:
fitfunc(formula = as.formula(x), family = ..1, data = data)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.7525  -0.6763  -0.3955   0.7997   2.3384  

Coefficients:
               Estimate Std. Error z value Pr(>|z|)   
(Intercept)      0.6515     2.6802   0.243  0.80794   
TNM_STAGEII     -2.2655     1.2897  -1.757  0.07899 . 
TNM_STAGEIII    -2.7072     1.2252  -2.210  0.02714 * 
TNM_STAGEIV     -3.6752     1.4965  -2.456  0.01405 * 
LIVER_PRE_PET1   2.4072     0.9164   2.627  0.00862 **
BMI             -0.2317     0.1080  -2.145  0.03196 * 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 76.823  on 58  degrees of freedom
Residual deviance: 56.599  on 53  degrees of freedom
AIC: 68.599

Number of Fisher Scoring iterations: 5
In [102]:
# Las variables que aparecen en el mejor modelo son:
var_sel_mod<-names(modelos_5top@objects[[5]]$coefficients)[-1]
print(var_sel_mod)

var_sel_mod1<-c("TNM_STAGE","LIVER_PRE_PET1","BMI")
[1] "TNM_STAGEII"    "TNM_STAGEIII"   "TNM_STAGEIV"    "LIVER_PRE_PET1"
[5] "BMI"           
In [103]:
# Calculamos el mejor modelo y su accuracy:

# Construimos la formula de forma automatica:
x = paste(var_sel_mod1, collapse = " + ")
y = respuesta
formBIC = as.formula(paste(y, "~", x))
formBIC
STATUS ~ TNM_STAGE + LIVER_PRE_PET1 + BMI
In [104]:
modelo1 <- train(formBIC, data=datos,method='glm')

    print("Matriz de confusión")
    pred <- predict(newdata=datos,modelo1) # prediccion usando el modelo logistico
    real <- as.factor(datos[,respuesta]) # respuesta real
    cm_train_TOT <- caret::confusionMatrix(data=pred,reference=real) # guardamos la matriz de confusion de training
    print(cm_train_TOT) # pintamos la matriz de confusion de training
[1] "Matriz de confusión"
Confusion Matrix and Statistics

          Reference
Prediction ALIVE DEATH
     ALIVE    33     9
     DEATH     5    12
                                          
               Accuracy : 0.7627          
                 95% CI : (0.6341, 0.8638)
    No Information Rate : 0.6441          
    P-Value [Acc > NIR] : 0.03564         
                                          
                  Kappa : 0.4594          
                                          
 Mcnemar's Test P-Value : 0.42268         
                                          
            Sensitivity : 0.8684          
            Specificity : 0.5714          
         Pos Pred Value : 0.7857          
         Neg Pred Value : 0.7059          
             Prevalence : 0.6441          
         Detection Rate : 0.5593          
   Detection Prevalence : 0.7119          
      Balanced Accuracy : 0.7199          
                                          
       'Positive' Class : ALIVE           
                                          

Feature selection Boruta¶

In [55]:
# Voy a definir el data frame de entrada: X
X = datos[-1]
head(X)

# Voy a definir la variable de salida:
y = as.factor(as.vector(unlist(datos[,respuesta])))
head(y)
A data.frame: 6 × 33
CYCLES_BETWEEN_PET1_PET2GENDERAGETNM_STAGEWBC_PRERBC_PREHB_PREPLT_PRECRP_PREALBUMIN_PRELDH_PREeGFR_PREAST_PREALT_PREK_PREBGL_PRE_PET1BMIBW_PRESPLEEN_FDG_UPT_PRE_PET1BM_UPT_PRE_PET1LIVER_PRE_PET1ESTIMATED_SPLEE_VOL_PRE_PET1DIAGNOSTICTREATMENTECOGPSCOMORBIDITIESCTCNCIACTION_TAKEN_TIME_BETWEE_PETdiasSLR_preBMLR_preOVERALL_TIME
<dbl><fct><dbl><fct><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><fct><fct><fct><fct><fct><fct><dbl><dbl><dbl><dbl><dbl>
13MALE 54III 3.823.03 9.701463.672.13573.060.116.8 6.74.87 9322.9066.52.672.862.45316.5GINECOLOGICALICISYMPTOMATIC BUT AMBULATORY HYPERLIPIDEMIA SEVERE DRUG INTERRUPTED2 250.85869571.0144928 2
22FEMALE79IV 10.873.9311.505154.112.65158.057.313.5 8.03.9210816.4733.52.402.702.70102.3HEAD AND NECKICIASYMTOMATIC HYPERTENSION NO SIDE EFFECTSDRUG INTERRUPTED4 01.00995020.930348350
33MALE 60III 8.404.41 8.563550.094.32 77.882.119.113.74.56 8720.1558.12.571.312.27 81.5LUNG CANCER ICISYMPTOMATIC,<50% IN BED DURING THE DAYDIABETES MELLITUS MODERATE DOSE REDUCED 4 140.99557521.0884956 2
43MALE 76II 4.663.5511.701780.103.70195.065.717.715.33.8313522.8356.72.252.462.26224.4RENAL CANCER ICIASYMTOMATIC HYPERTENSION NO SIDE EFFECTSDOSE NOT CHANGED6 00.88114750.717213112
55FEMALE70II 5.865.1915.103437.263.94166.079.918.110.23.8311118.2941.72.482.522.55260.0MELANOMA ICISYMPTOMATIC BUT AMBULATORY DIABETES MELLITUS NO SIDE EFFECTSDOSE NOT CHANGED6 00.82692311.019230812
64MALE 54IV 2.954.4514.501670.254.16135.076.314.414.04.55 9827.1880.42.011.402.83198.4LUNG CANCER ICIASYMTOMATIC CANCER RRECURRENCEHIGHT SEVERE DOSE NOT CHANGED63631.13215860.577092518
  1. ALIVE
  2. ALIVE
  3. ALIVE
  4. ALIVE
  5. ALIVE
  6. ALIVE
Levels:
  1. 'ALIVE'
  2. 'DEATH'
In [56]:
# Determinar la importancia de los atributos.
boruta.model <- Boruta(y~., data = cbind(X,y), doTrace = 2)
 1. run of importance source...

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After 12 iterations, +0.32 secs: 

 rejected 28 attributes: ALBUMIN_PRE, ALT_PRE, AST_PRE, BGL_PRE_PET1, BM_UPT_PRE_PET1 and 23 more;

 still have 5 attributes left.


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After 16 iterations, +0.39 secs: 

 rejected 1 attribute: ECOGPS;

 still have 4 attributes left.


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After 35 iterations, +0.69 secs: 

 rejected 1 attribute: eGFR_PRE;

 still have 3 attributes left.


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After 65 iterations, +1.2 secs: 

 confirmed 1 attribute: TNM_STAGE;

 still have 2 attributes left.


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In [57]:
options(repr.plot.width=12, repr.plot.height=8)

print(boruta.model)
plot(boruta.model)
Boruta performed 99 iterations in 1.677527 secs.
 1 attributes confirmed important: TNM_STAGE;
 30 attributes confirmed unimportant: ALBUMIN_PRE, ALT_PRE, AST_PRE,
BGL_PRE_PET1, BM_UPT_PRE_PET1 and 25 more;
 2 tentative attributes left: ACTION_TAKEN_, AGE;
In [58]:
# Refinar modelo para resolver posibles atributos tentativos.
# Dado que pueden existir atributos no resueltos (tentativos), refinamos el modelo.

boruta.model2 <- TentativeRoughFix(boruta.model)
print(boruta.model2)
plot(boruta.model2)
Boruta performed 99 iterations in 1.677527 secs.
Tentatives roughfixed over the last 99 iterations.
 1 attributes confirmed important: TNM_STAGE;
 32 attributes confirmed unimportant: ACTION_TAKEN_, AGE, ALBUMIN_PRE,
ALT_PRE, AST_PRE and 27 more;
In [59]:
# Obtener una lista de los atributos y sus etiquetas con el analisis de Boruta
as.data.frame(boruta.model$finalDecision)
A data.frame: 33 × 1
boruta.model$finalDecision
<fct>
CYCLES_BETWEEN_PET1_PET2Rejected
GENDERRejected
AGETentative
TNM_STAGEConfirmed
WBC_PRERejected
RBC_PRERejected
HB_PRERejected
PLT_PRERejected
CRP_PRERejected
ALBUMIN_PRERejected
LDH_PRERejected
eGFR_PRERejected
AST_PRERejected
ALT_PRERejected
K_PRERejected
BGL_PRE_PET1Rejected
BMIRejected
BW_PRERejected
SPLEEN_FDG_UPT_PRE_PET1Rejected
BM_UPT_PRE_PET1Rejected
LIVER_PRE_PET1Rejected
ESTIMATED_SPLEE_VOL_PRE_PET1Rejected
DIAGNOSTICRejected
TREATMENTRejected
ECOGPSRejected
COMORBIDITIESRejected
CTCNCIRejected
ACTION_TAKEN_Tentative
TIME_BETWEE_PETRejected
diasRejected
SLR_preRejected
BMLR_preRejected
OVERALL_TIMERejected
In [60]:
# Obtener una lista de los atributos importantes y tentativos
opc_boruta = getSelectedAttributes(boruta.model2, withTentative = F)
opc_boruta
'TNM_STAGE'
In [61]:
# Construimos la formula de forma automatica:
x = paste(opc_boruta, collapse = " + ")
y = respuesta
formBoruta = as.formula(paste(y, "~", x))
formBoruta
STATUS ~ TNM_STAGE

Curva Roc¶

In [105]:
library(ROCR)
In [1048]:
index = sample(1:nrow(datos), size = .100 * nrow(datos))
#index
train = datos[index, ]
test = datos[-index, ]

model = glm(STATUS~TNM_STAGE+ACTION_TAKEN_+DIAGNOSTIC+LIVER_PRE_PET1+dias+BMI+CYCLES_BETWEEN_PET1_PET2,data=train, 
            family = binomial(link = "logit"))

pred = predict(model,test,type="response")
pred = prediction(pred,test$STATUS)
perf = performance(pred, "acc")
#plot(perf)

max_ind = which.max(slot(perf, "y.values")[[1]] )
acc = slot(perf, "y.values")[[1]][max_ind]
cutoff = slot(perf, "x.values")[[1]][max_ind]
print(c(accuracy= acc))

perf_cost = performance(pred, "cost")
perf_err = performance(pred, "err")
perf_tpr = performance(pred, "tpr")
perf_sn_sp = performance(pred, "sens", "spec")

roc = performance(pred,"tpr","fpr")
plot(roc, colorize = T, lwd = 2)
abline(a = 0, b = 1)

auc = performance(pred, measure = "auc")
print(auc@y.values)
 accuracy 
0.7407407 
[[1]]
[1] 0.7661765

CONCLUSIONES¶

Aqui nos pasa lo mismo que con el modelo con los datos de DIFF.
Los graficos es lo que mas nos puede aportar como conclusiones mas intesantes.